Organizational contact information
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Fax
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Mail
Statistics Canada
150 Tunney's Pasture Driveway
Ottawa, Ontario
K1A 0T6
Telephone
Toll-free: 1-800-263-1136
International: 1-514-283-8300
TTY: 1-800-363-7629
Fax
1-877-287-4369 or 613-951-0581
Mail
Statistics Canada
150 Tunney's Pasture Driveway
Ottawa, Ontario
K1A 0T6
For Departmental Plans and Departmental Results Reports, planned spending refers to those amounts presented in Main Estimates.
A department is expected to be aware of the authorities that it has sought and received. Determining planned spending is a departmental responsibility, and departments must be able to defend the expenditure and accrual numbers presented in their Departmental Plans and Departmental Results Reports.
Discussion on the following themes by three experts:.
Panelists: Eric Deeben, Office of National Statistics, Data Science Campus, United Kingdom, Wendy Martinez, Bureau of Labor Statistics, USA and Danny Pfeffermann, Central Bureau of Statistics, Israel
Moderator: Eric Rancourt, Statistics Canada, Canada
Eric Deeben is the Technical International Programme Lead and Synthetic Data & Privacy Preservation Techniques Squad Lead at the Data Science Campus of the Office for National Statistics of the UK.
As Technical International Programme Lead, Eric engages with other National Statistical Organisations (NSOs) and international bodies. This is in an effort to exchange new data science methods e.g. machine learning, and implement architecture principles with the objective to move from producing exploration statistics to official statistics.
Eric is a dynamic and highly skilled Solution Owner skilled in achieving business and customer objectives. An excellent communicator with international and multi-cultural work experience across Europe, the Americas and Africa. Eric has presented and lectured across the United Kingdom, Norway, Netherlands, Switzerland and United States. Eric is a notable Project Manager with global delivery experience and is an associate lecturer at Cardiff Metropolitan University Business School.
Eric will be sharing his experiences from his participation and leadership within the international Machine Learning field. He will outline some of the processes in play for managing the impactful implementation of machine learning at the NSO, and the importance of international collaboration.
Wendy Martinez has been serving as the Director of the Mathematical Statistics Research Center at the US Bureau of Labor Statistics (BLS) for eight years. Prior to this, she served in several research positions throughout the US Department of Defense. She held the position of Science and Technology Program Officer at the US Office of Naval Research, where she established a research portfolio comprised of academia and industry performers developing data science products for the future Navy and Marine Corps. Her areas of interest include computational statistics, exploratory data analysis, and text data mining. She is the lead author of three books on MATLAB and statistics. Dr. Martinez was elected as a Fellow of the American Statistical Association (ASA) in 2006 and is an elected member of the International Statistical Institute. She also had the honor of serving as the President of the American Statistical Association in 2020.
Danny Pfeffermann is the National Statistician and Director General of Israel's Central Bureau of Statistics (CBS). He is Professor Emeritus of Statistics at the Hebrew University of Jerusalem and Professor of Social Statistics at the University of Southampton. His main research areas are: Analytic inference from complex sample surveys; Seasonal adjustment and trend estimation; Small area estimation; Inference under informative sampling and nonresponse and more recently; Mode effects and Proxy surveys. Professor Pfeffermann published about 80 articles in leading statistical journals and co-edited the two-volume handbook on Sample Surveys. He is Fellow of the American Statistical Association (ASA), the International Statistical Institute (ISI) and the Institute of Mathematical Statistics (IMS) and recipient of several international awards.
Statistics Canada would like to thank Canadians and businesses and the interviewing staff, and recognize their support. The information provided was converted into statistics used by Canadians, businesses and policy makers to make informed decisions about employment, education, health care, market development and more.
Never has the role of data—and data-driven insights—been more important in supporting Canadians in their time of need than during the COVID-19 pandemic. When the first wave hit in March 2020, data immediately went from being a nice-to-have asset to a critical decision-support tool. Virtually overnight, Statistics Canada employees pivoted to the new reality by rapidly adapting operations to better serve Canadians. This report outlines how Statistics Canada has responded to the nation's urgent demands for data over the course of a rapidly evolving public health emergency.
In particular, the agency has delivered results for Canadians on the following priorities for 2020–21:
The agency is continuously sharing information about what it does and how it goes about providing high-quality statistics. Statistics Canada's commitment to privacy and transparency continues to be strengthened through the Proportionality and Necessity Framework and the Trust Centre. I invite Canadians to see how Statistics Canada uses their data responsibly to provide the fact base they need to make informed decisions.
The need for timely and accurate data has never been greater in revealing whether Canada is on the right track as the nation, and its economy and society, gradually recovers from the pandemic.
I invite you to learn more through this report and the article "COVID-19 in Canada: A One-year Update on Social and Economic Impacts" to learn how Statistics Canada delivered better data to drive better outcomes for the people of Canada during COVID-19.
Anil Arora
Chief Statistician of Canada
The Honourable François-Philippe Champagne,
Minister of Innovation, Science and Industry
It is our pleasure to present the 2020–21 Departmental Results Report for Statistics Canada.
In a year that was characterized by uncertainty and rapidly shifting priorities as a result of the global COVID-19 pandemic, Innovation, Science and Economic Development Canada (ISED) and its Portfolio partners remained committed in their continued efforts to meet the evolving needs of Canadians and the Canadian economy. The ISED and Portfolio Departmental Results Reports describe a number of immediate and remarkable contributions over the past year, including those that were part of Canada's COVID-19 Economic Response Plan.
To meet the urgent data needs of Canadians, Statistics Canada worked to establish key partnerships to develop innovative approaches to data collection, analysis and integration beyond its traditional survey-first approach. Over the course of 2020–21, the agency collaborated with all levels of government, civil society groups and the private sector to provide data-driven insights that have informed the pandemic response and continue to shape the country's recovery.
In 2020–21, Statistics Canada played an important role in informing the government's pandemic response by disaggregating large datasets, revealing that COVID-19 has not affected all Canadians in the same ways. These insights will continue to inform policy decisions for years to come.
Statistics Canada quickly adapted to ensure that the 2021 Census of Population could be conducted during a pandemic safely, securely and remotely. The data collected will be crucial to policy and decision makers, as Canadians continue to deal with the impacts of the COVID-19 pandemic.
Through all these initiatives and more, we continued to deliver on our commitment to foster a dynamic and growing economy that creates jobs, opportunities and a better quality of life for all Canadians, including those from diverse backgrounds, such as women, Indigenous peoples, racialized Canadians, persons with disabilities and LGBTQ+ groups.
We invite you to read this report to learn more about how Statistics Canada, like ISED and other Portfolio partners, is building a strong culture of innovation to position Canada as a leader in the global economy.
The publication of the National Occupational Classification (NOC) 2021 is the thirtieth anniversary of the standard occupational classification system and it introduces a major structural change. The NOC 2021 Version 1.0 overhauls the "Skill Level" structure by introducing a new categorization representing the degree of Training, Education, Experience and Responsibilities (TEER) required for an occupation. The NOC 2021 Version 1.0 also introduces a new 5-digit hierarchical structure, compared to a 4-digit hierarchical structure in the previous versions of the classification. This revision is extensive; the last structural revision was NOC 2011.
The National Occupational Classification (NOC) 2021 is published, in partnership by Employment and Social Development Canada (ESDC) and Statistics Canada (StatCan). The NOC 2021 Version 1.0 has been made possible through the significant contributions of a number of individuals and groups, including a team of occupational research analysts and assistants from both ESDC and StatCan. Their commitment to excellence is evident in this new version of the NOC's foundational system used for structuring and describing occupations in the Canadian labour market and for managing the collection, analysis and reporting of occupational statistics. The partnership and collaboration between ESDC and StatCan has ensured that quantitative and qualitative information on occupations continues to be reliable, timely and relevant for a wide range of audiences.
The National Occupational Classification (NOC) Canada is a classification of occupations designed primarily for use in statistical programs. It is also used for employment-related program administration and to compile, analyze and communicate information about occupations, such as labour market information. Occupational information is of critical importance for the provision of labour market and career intelligence, occupational forecasting, labour supply and demand analysis, employment equity, job training and skills development, and numerous other programs and services. It provides a standardized framework for organizing the world of work, for pay or profit, in a manageable, understandable and coherent system.
Statistical classifications are comprehensive structured lists of mutually exclusive categories or classification itemsFootnote 1. In practice, this means that there is always a category in the classification for an object that falls within the scope of the classification, and that the object can be classified in only one category. The section titled "The underlying concepts" further discusses the object and scope of NOC.
The structure of the NOC is hierarchical. This type of classification system enables the collection, analysis and publication of data at different levels of detail, in a standardized way. The section titled "The classification structure and coding system" discusses the structure of the NOC in greater detail.
The purpose of standard classifications is to support the integration of data obtained from multiple sources by organizing the documentation, collection, processing, presentation and analysis of data in a systematic manner. Classifications are essential elements of a coherent and efficient statistical system.
The NOC has been developed to support the integration of occupational statistics. The next section provides a background on the NOC, the potential impact of the redesign and a sense of the different applications of the classification.
The NOC was jointly developed by Employment and Social Development Canada (ESDC) and Statistics Canada (StatCan), and has been maintained in partnership since the first edition published in 1991/1992. Prior to 2011, ESDC NOC and StatCan NOC-S differed in their major group structures and, consequently, in their coding systems. However, the revised NOC 2011 eliminated the differences between the two former systems.
As the Canadian economy and society changes, it is common practice for classifications to be updated and revised as new industries, products, occupations and educational programs are introduced. NOC structural revisions are planned every 10 years and content was updated every 5 years to respond to labour market changes. Starting in 2017, the NOC 2016 has undergone content updates approximately every year to ensure users have the most up-to-date information. This publication of the National Occupational Classification (NOC) 2021 represents a major structural revision of the NOC based on its 10-year revision cycle.
The NOC 2016 structure and format are based on a four-tiered hierarchical arrangement of occupational groups with successive levels of disaggregation. It contains broad occupational categories, major, minor and unit groups. It is categorized based on two major attributes of jobs, the "Broad Occupational Category" and the "Skill Level", as classification criteria. The former was defined as the type of work performed, with respect to the educational discipline or field of study for entry into an occupation and the industry of employment (e.g. health occupations or sales and service occupations). The "Skill Level" categorization is primarily defined by the amount and type of education and training usually required to enter and perform the duties of an occupation, but also considers experience, complexity and responsibilities. See NOC 2016 Structure below.
Broad Occupational Category or the skill type criteria is… | when the first digit is… |
---|---|
Management occupations | 0 |
Business, finance and administration occupations | 1 |
Natural and applied sciences and related occupations | 2 |
Health occupations | 3 |
Occupations in education, law and social, community and government services | 4 |
Occupations in art, culture, recreation and sport | 5 |
Sales and service occupations | 6 |
Trades, transport and equipment operators and related occupations | 7 |
Natural resources, agriculture and related production occupations | 8 |
Occupations in manufacturing and utilities | 9 |
Skill Level category or the skill level criteria is… | when the second digit is… |
---|---|
Skill Level A | 0 or 1 |
Skill Level B | 2 or 3 |
Skill Level C | 4 or 5 |
Skill Level D | 6 or 7 |
The NOC 2021 Version 1.0 was developed through ongoing discussions between ESDC and StatCan as well as consultations with stakeholders. During consultations leading toward the NOC 2021 revision, it was suggested to add a new "Level" to the NOC 2016 Skill level categorization, to clarify the distinction in formal training or education required among unit groups, especially in the current "Skill Level B", which has a wide range of formal training or educational requirements. Over time due to the changing world of work, the NOC 2016 "Skill Level B" became a disproportionately large group and thereby limited the ability to analyze distinctions amongst a large percentage of occupations.
Another observation during the revision process was the use of the "Skill Level" categorization in the NOC as possibly being misleading because training and education, which are the main building blocks of the NOC's "Skill Level" categorization, are not considered as "skills" in the labour market. With regards to skills, many countries and organizations are currently developing their own skills taxonomy (which include concepts such as numeracy and literacy). Therefore, it was deemed appropriate for the NOC to move away from the "Skill Level" categorization.
The NOC 2021 Version 1.0 represents a major structural revision whereby the existing occupational groups are reviewed alongside input collected from many relevant stakeholders through consultation. The main accomplishment of the NOC 2021 Version 1.0 was the overhaul of the "skill level" categorization by introducing a new categorization representing the degree of Training, Education, Experience and Responsibilities (TEER) required for an occupation.
The redesign of the NOC for 2021 moves away from the previous version of NOC with four "Skill Level" categories to an innovative six-grouping "TEER" categorization. This change is necessary for several reasons. First, the focus of the NOC is occupations and not skills and the "Skill Level" terminology is often misleading for many stakeholders. This change will reduce confusion. Second, some NOC users artificially create or infer a low- versus high-skill categorization. This redesign moves away from high/low skill categorization as the TEER more accurately captures differences in occupational requirements, which in turn will aid in the analysis of occupations.
The section titled "The classification structure and coding system" discusses the structure of the NOC 2021 Version 1.0 in greater detail.
These changes significantly improve how the NOC classification takes into account the distinctions in formal training and educational requirements and better reflects skill and knowledge development occurring through on-the-job experience. At the same time, it increases the homogeneity of the distribution of unit groups within the classification, and addresses concerns about the "Skill Level" categorization and the distribution of unit groups among them.
The transition from the "Skill Level" to the "TEER" categorization makes the distribution of occupations across the "TEER categories" more balanced. The change in the distribution of unit groups is summarized in the tables below.
NOC 2016 V1.3 Distribution of Unit Groups by Skill Level | NOC 2021 V1.0 Distribution of Unit Groups by TEER | ||
---|---|---|---|
TEER Category 0 | 9% | ||
Skill Level A | 28% | TEER Category 1 | 19% |
Skill Level B | 42% | TEER Category 2 | 31% |
Skill Level C | 24% | TEER Category 3 | 13% |
Skill Level D | 6% | TEER Category 4 | 18% |
TEER Category 5 | 9% |
The NOC is the nationally accepted taxonomy and organizational framework of occupations in the Canadian labour market. It is important to note that the redesign of the NOC can have significant implications for several Surveys, such as the Labour Force Survey (LFS), and ESDC programs such as the Temporary Foreign Worker Program and Employment Insurance program. This change may have significant impact on various programs throughout other federal departments, as well as provincial, territorial and municipal governments and many users of the NOC.
The NOC 2021 Version 1.0, as of August 10th, 2021, is now the departmental standard for data collection and dissemination for occupations at Statistics Canada. Implementation dates for each new classification version such as NOC 2021 Version 1.0 will vary based on when statistical surveys or programs, entities, organizations or individuals decide to use it.
Statistical classifications are built around three basic concepts: the object classified or statistical unit, the scope or universe of the classification, and the criteria used to group statistical units in standard categories or classification items.
The basic principle of the NOC is the kind of work performed. The statistical unit or object being classified using the NOC is the concept of a "job". A job encompasses all the tasks carried out by a particular person to complete their duties. A job title represents the name given to a job or a position. The term job is used in reference to employment or in self-employment.
An occupation is defined as a collection of jobs, sufficiently similar or identical in work or tasks performed to be grouped under a common label for classification purposes.
The scope of the NOC is all occupations and jobs in the Canadian labour market undertaken for pay or profit, including people who are self-employed.
The NOC is not designed to include work or tasks not undertaken for pay or profit, for example, voluntary work. However, a person may complete work not for pay or profit where the tasks completed may be described within some occupational groups.
The classification criteria refers to the attribute(s) of the statistical unit used to create the most detailed categories of the classification and to group them into analytical aggregates. These attributes must be observable and verifiable in the context of a statistical operation, or it must be possible to derive the information as a set of observed characteristics.
The NOC is built through the application of two major attributes of jobs as classification criteria: broad occupational categories and TEER categories. There are ten broad occupational categories and six TEER categories.
The next section "The classification structure and coding system" provides more detail on how these two criteria together create the classification structure.
The NOC contains a standard classification structure and standard variants of that structure. The standard structure is intended for broad use, whereas each variant is designed to meet a specific user need. The NOC provides a systematic classification structure that categorizes the entire range of occupational activity in Canada. Its' detailed occupations are identified and grouped primarily according to the work performed, as determined by the training, education, tasks, experiences, duties and responsibilities for an occupation.
The standard classification structure uses a five-tiered hierarchical arrangement of occupational groups with successive levels of disaggregation and contains broad, major, sub-major, minor and unit groupings. The structure of the NOC 2021 Version 1.0 is based on two key occupational categorizations: Occupational categories and TEER categories, which are identified in the first two digits of the 5-digit code, as shown in the table below.
Title of Hierarchy | Format | Digit | Represents: |
---|---|---|---|
Broad Category | X | First Digit – X | Occupational categorization |
Major Group | XX | Second Digit xX | TEER categorization |
Sub-major Group | XXX | xxX | Top level of the Sub-Major Group |
Minor Group | XXXX | xxXX | Hierarchy within the Sub-Major Group |
Unit Group | XXXXX | xxXXX | Hierarchy within the Minor Group |
Note: As the first digit identifies the Occupation and the second digit identifies the TEER, they are also referred to as the Major Group.
The hierarchical approach of the NOC ensures collection, dissemination and analysis of data at different levels of detail, in a standardized way. Each number or digit at each hierarchical level has a distinct meaning. Each hierarchical level of the classification is described below, from the most detailed to the most aggregated level.
Level | Coding | Number of categories |
---|---|---|
Broad Category | 1-digit and first digit of all codes | 10 |
TEER | Second-digit of all codes | 6 |
Major Group | 2-digit representing the broad category code and the TEER code | 45 |
Sub-major Group | 3-digit | 89 |
Minor Group | 4-digit | 162 |
Unit Group | 5-digit | 516 |
Broad categories
The Broad Category (first digit) of the classification represents the occupational categorization which is defined by the type of work performed, the field of study, or the industry of employment. There are 10 Broad categories in NOC 2021 Version 1.0.
TEER categories
The TEER Category (second digit) of the classification represents the necessary training, education, experience and responsibilities of the occupation. There are 6 TEER categories in NOC 2021 Version 1.0.
Major groups
The Major Group (first and second digits) of the classification is represented by the Broad occupational categorization (first digit) and TEER categorization (second digit) together. A major group also encompasses several sub-major groups and thus represents the two-digit code used by the NOC. There are 45 major groups in NOC 2021 Version 1.0.
Sub-major groups
The Sub-major Group (3-digit) of the classification represents the aggregation of several minor groups and thus represents the three-digit code used by the NOC. There are 89 sub-major groups in NOC 2021 Version 1.0.
Minor groups
The Minor Group (4-digit) of the classification represents the domain in which an occupation is carried out (occupational domain). It is an aggregation of several unit groups and thus represents the four-digit code used by the NOC. There are 162 minor groups in NOC 2021 Version 1.0.
Unit Groups
The Unit Group (5-digit) of the classification is the most detailed level of the classification and represents one or several occupations combined together within the NOC. There are 516 units groups in NOC 2021 Version 1.0.
Level | NOC 2021 V1.0 Code | NOC 2021 V1.0 Title |
---|---|---|
Broad occupational group | 4 | Occupations in education, law and social, community and government services |
Major group | 41 | Professional occupations in law, education, social, community and government services |
Sub-minor group | 411 | Professional occupations in law |
Minor group | 4110 | Judges, lawyers and Quebec notaries |
Unit Group | 41100 | Judges |
Unit Group | 41101 | Lawyers and Quebec notaries |
As identified in the "Classification Criteria" the NOC 2021 Version 1.0 is built through the application of two major attributes of jobs as classification criteria: ten broad occupational categories (BOC) and six TEER categories.
Broad Occupational Categories are defined as the type of work performed based on, notably, the field of study required for entry into an occupation and the industry of employment. The ten BOCs are classified from 0 to 9.
NOC 2021 V1.0 Broad Category - Occupation | when the first digit is… |
---|---|
BOC 0 - Legislative and senior management occupations This broad category comprises legislators and senior management occupations. |
0 |
BOC 1 - Business, finance and administration occupations This broad category comprises specialized middle management occupations in administrative services, financial and business services and communication (except broadcasting), as well as professional occupations in financial and business; administrative and financial supervisors and specialized administrative occupations; administrative occupations and transportation logistics occupations; and office and administrative support and supply chain logistics occupations. |
1 |
BOC 2 - Natural and applied sciences and related occupations This broad category comprises occupations in natural sciences (including basic and applied sciences and experimental development), engineering, architecture and information technology. These occupations cover specialized middle management occupations in engineering, architecture, science and information systems; professional occupations in natural sciences (basic and applied sciences and experimental development); and technical occupations related to natural sciences (including basic and applied sciences and experimental development). |
2 |
BOC 3 - Health occupations This broad category comprises specialized middle management occupations in health care, as well as occupations concerned with providing health care services directly to patients (professional and technical occupations in health) and occupations that provide support to health services. |
3 |
BOC 4 - Occupations in education, law and social, community and government services This broad category comprises managers in public administration, in education and social and community services and in public protection services, as well as occupations concerned with teaching, law, counselling, conducting social science research, developing government policy, and administering government and other programs, and related support occupations. |
4 |
BOC 5 - Occupations in art, culture, recreation and sport This broad category comprises specialized middle management occupations in art, culture, recreation and sport, as well as professional, technical, support and other occupations concerned with art and culture (including the performing arts, film and video, broadcasting, journalism, writing, creative design, libraries and museums), recreation and sports. |
5 |
BOC 6 - Sales and service occupations This broad category comprises middle management occupations in wholesale and retail trade, and customer services, as well as occupations concerned with wholesale and retail sales, and customer, personal and support service occupations related to a wide range of trade and service industries, such as accommodation and food services, travel, tourism and cleaning services. |
6 |
BOC 7 - Trades, transport and equipment operators and related occupations This broad category comprises middle management occupations in trades, transportation and equipment, as well as occupations such as technical trades and transportation officers and controllers; general trades; mail and message distribution, other transport equipment operators and related maintenance workers; and helpers and labourers and other transport drivers, operators and labourers. |
7 |
BOC 8 - Natural resources, agriculture and related production occupations This broad category comprises middle management occupations in natural resources, agriculture and related production, as well as occupations concerned with supervision and equipment operation in the natural resource-based sectors of mining, oil and gas production, forestry and logging, agriculture, horticulture and fishing. Harvesting, landscaping and natural resources labourers are also included. Most occupations in this category are industry specific and do not occur outside of the primary resources industries. |
8 |
BOC 9 - Occupations in manufacturing and utilities This broad category comprises middle management occupations in manufacturing and utilities, as well as occupations concerned with supervisory, production and labouring in manufacturing, processing and utilities. |
9 |
The "TEER" categorization defines the requirements of the occupation by considering the type of training, education and experience required for entry, as well as the complexities and responsibilities typical of an occupation. In general, the greater the range and complexity of occupational tasks, the greater the amount of formal education and training, previous experience, on-the-job training, and in some instance's responsibility, required to competently perform the set of tasks for that occupation.
The NOC 2021 V1.0 Training, Education, Experience and Responsibility (TEER) | when the second digit is… |
---|---|
Management - TEER | 0 |
Completion of a university degree (bachelor's, master's or doctorate); or Previous experience and expertise in subject matter knowledge from a related occupation found in TEER 2 (when applicable). |
1 |
Completion of a post-secondary education program of two to three years at community college, institute of technology or CÉGEP; or Completion of an apprenticeship training program of two to five years; or Occupations with supervisory or significant safety (e.g. police officers and firefighters) responsibilities; or Several years of experience in a related occupation from TEER 3 (when applicable). |
2 |
Completion of a post-secondary education program of less than two years at community college, institute of technology or CÉGEP; or Completion of an apprenticeship training program of less than two years; or More than six months of on-the-job training, training courses or specific work experience with some secondary school education; or Several years of experience in a related occupation from TEER 4 (when applicable). |
3 |
Completion of secondary school; or Several weeks of on-the-job training with some secondary school education; or Experience in a related occupation from TEER 5 (when applicable). |
4 |
Short work demonstration and no formal educational requirements. | 5 |
This section outlines the conventions adopted in order to assist users in consistently assigning NOC 2021 Version 1.0 codes and titles.
When a unit group ends with a "9", it is used to classify occupations in an appropriate "other" occupation when a grouping does not account for all the workers in a group, even though such workers may perform distinct sets of work activities. These occupational groups are identified in their title by ''Other'' appearing at the beginning of the title. "Other" titles exist at the sub-group, minor group or unit group level, for example, Sub-major group 729 – Other technical trades; Minor group 2139 – Other engineers; and Unit group 32209 - Other practitioners of natural healing.
The NOC is available separately in both official languages. It is important to note that the French version includes only titles commonly used in French and proper to the milieu and, therefore, these are not normally translations of the English titles. The classification structure is the same in both languages.
Unit group labels, example titles, inclusions and exclusions are presented in a gender-neutral format or identified by the masculine and feminine titles separated by a slash.
Modifying terms have been added to several job titles, as extensions, to designate the industrial sector or the domain of expertise. If applicable, this information is preceded by a dash at the end of the title (cashier supervisor - retail) to distinguish between similar titles. These modifying terms may also specify where the titles appear in the classification structure (painter - visual arts; painter - motor vehicle repair). This information should be considered when coding job titles.
As a general rule, the class of worker status, that is, whether the respondent works for wages or is self-employed, is not considered for classification purposes.
An exception is made for proprietors in retail trade, food and accommodation services, and residential home building. These are classified as managers to the following unit groups:
Supervisors are generally classified with the workers supervised and as a result would not have a separate unit group. But there are exceptions to this convention, for example, unit groups 31300 - Nursing coordinators and supervisors and 62010 – Retail sales supervisors.
Supervisors in the following occupational categories have been classified in supervisor unit groups separate from the workers supervised:
Even when separate supervisory unit groups exist, "lead hands" are not classified as such, as previous research has indicated that supervision is usually only a minor part of such jobs.
Generally, inspectors with TEER 2 requirements have been classified in separate unit groups or with technicians and technologists, with matching requirements. Other non-technical inspectors, testers, graders and samplers have been included either in separate unit groups covering occupations in processing industries or in unit groups of assemblers and fabricators in manufacturing industries. This is reflective of patterns of employment found within industries and the increasing responsibility for quality control that is placed on manufacturing production workers.
Apprentices and trainees have been classified in the same unit groups as the occupations for which they are training. Similarly, interns, residents and articling students are classified with their respective professional groups.
This convention has been adopted to prevent a proliferation of unit groups of apprentices. It is not intended to imply equivalence or interchangeability of apprentices or trainees with fully qualified workers.
Each NOC unit group description consists of several standardized sections which define and describe its content.
This section provides a general description of the content and boundaries of the unit group and indicates the main activities of occupations within the unit group. It also indicates the kinds of industries or establishments in which the occupations are found. The list of places of employment is not always exhaustive, but can assist in clarifying the occupations described and in differentiating them from occupations found in other groups.
This section is a list of titles commonly used in the labour market. The titles are intended to illustrate the contents and range of the occupational group. This is not an exhaustive list of job titles.
This section provides a list of borderline job titles belonging to a particular NOC unit group. Inclusions are examples in classes where it might not be clear from reading both the class text and title that the example belongs in the class.
This section clarifies the boundaries of the unit group by identifying related unit groups and similar occupations that are classified elsewhere. Unit groups or individual occupations are cited in this section when they bear a functional similarity to the unit group or when similar titles occur.
The main duties section describes the most significant duties of the occupations in the group. They are not intended to be comprehensive of all the tasks performed in the occupation. They represent key duties that are related to the occupation(s) associated with the unit group and can be listed using:
Statements in italics, at the end of this section, identify a specialization that may exist within the occupation.
This section describes the employment requirements for the unit group. Several types of requirements are identified in this section and are listed in the following order.
Some occupations have very definite employment requirements while for others, there is no consensus or a range of acceptable requirements exist. The following terminology is used to indicate the level of the requirement:
Employment requirements items to note:
This section appears in some unit group descriptions. It provides information on the following:
The NOC is designed to classify occupational information in the Canadian labour market in a standardized framework and a manageable, understandable and coherent system. Statistics Canada's role is to provide occupational information for statistical surveys and data analysis.
When conducting a search to determine what code is best associated with a job title or occupation it is important to note:
Both of which are determined by the main duties, tasks and responsibilities of the occupation and are key when trying to determine the best code for a job title. Consideration of these factors has proven to be effective in helping to narrow the search for a desired NOC code.
The classification of occupations does not stand alone but must be understood as being related to other classifications, such as the North American Industry Classification System (NAICS) and that of Class of Worker. Each of these classifications supplements the NOC 2021 Version 1.0 in presenting a rounded picture of the nature of a person's job.
The industrial qualifier which may accompany the job title:
The industry in which the individual is employed is determined by the kind of economic activity of the establishment. The establishment is usually a factory, mine, farm, store, other place of business or an institution for which a number of basic production variables can be compiled.
It is important to note the conceptual differences between an industry classification and an occupation classification. An establishment can employ individuals performing completely different occupations, and these are classified to appropriate occupational groups, but the industrial classification of each individual employed in the establishment should be the same and is determined by the nature of the production process of the establishment. In other words, the nature of the factory, business or service in which the person is employed does not determine the classification of the occupation, except to the extent that it enables the nature of the duties to be more clearly defined, but it does determine the classification of the establishment by industry.
Class of worker refers to an individual's employment relationship to the business in which they work, as employee or self-employed, including unpaid family workers, and thus provides another means of describing the work. The NOC 2021 Version 1.0 does not indicate the class of worker classification for each occupation since many occupations contain both jobs held by employees and jobs of self-employed individuals. The scope of what is an occupation has been outlined in the section "The underlying concepts".
The NOC is comparable to the International Standard Classification of Occupations (ISCO) published by the International Labour Organization (ILO). While the NOC was originally developed in Canada in the 1980s, ISCO was also being reviewed and updated to produce ISCO-88. Communication between the NOC and ISCO research teams led to similarities, such as a similar conceptual framework that includes the Skill Type and Skill Level dimensions. The similarities between the NOC and ISCO increased in later structural revision (ISCO-08 and NOC 2011) cycles. However, certain conceptual differences between the NOC 2021 Version 1.0 and ISCO-08 limit comparability. For instance, differences in the classification criteria and classification structure exist between NOC 2021 Version 1.0 and ISCO-08. Additionally, subsistence occupations included in ISCO are not part of the NOC. For countries and regions in which subsistence activities are virtually non-existent, the ILO affirms that such activities may be excluded without loss of international comparability.
The concordance between NOC 2011 and ISCO 2008 can be used for purposes of showing the relationship between NOC 2016 and ISCO 2008 since the structure is the same in both NOC 2011 and NOC 2016. A correspondence table is available between NOC 2016 V1.3 and NOC 2021 V1.0; a correspondence table between NOC 2021 V1.0 and ISCO-08 will be developed and published.
Changes occurred at all levels of the NOC structure. Some items were revised while others were added, split, transferred or merged. A complete list of all changes detailed at the unit group level (the most detailed level of the NOC structure) is released as a separate correspondence file and is available at NOC 2016 V1.3 - NOC 2021 V1.0.
The following are some examples of higher level structural changes:
In the 2016 version of the NOC, "BOC 0 – Management" included all unit groups dedicated to managerial occupations. Under the 2021 NOC, management is identified in the employment requirements, via the TEER system, rather than as a "field of study and/or industry". The BOC 0 now only contains legislators and senior management occupations. Middle management occupations previously in BOC 0 have been redistributed into the remaining 9 BOCs based on the industry of employment. For example: 0211 - Engineering managers (NOC 2016 V1.3) has been moved to BOC 2 – Natural and applied sciences and related occupations, to unit group 20010 – Engineering managers (NOC 2021 V1.0).
Another example of a higher level structural change is NOC 2016 Version 1.3 minor group 227 - Transportation officers and controllers (and associated unit groups) was moved from BOC 2 - Natural and applied sciences and related occupations to BOC 7 - Trades, transport and equipment operators and related occupations as NOC 2021 Version 1.0 minor group 7260 - Transportation officers and controllers (and associated unit groups). This move better aligns the minor occupation group with the industry of employment.
As it is done for all standard statistical classifications, we provide a more detailed analysis of the changes at the lowest of the classification, which is the unit group level for the NOC.
The unit group level (5-digit) is the most detailed level of the NOC and it is used for the analysis of the changes in a more detailed manner. Some items were revised while others were added, split, transferred or merged. The Generic Statistical Information Model (GSIM) is used to identify the types of changes made to the classification: real changes and virtual changes. Real changes are those affecting the scope of the existing classification items or categories, whether or not accompanied by changes in the title, definition and/or the coding. Virtual changes are those made in coding, titles and/or definitions, while the meaning or scope of the classification item remains the same. The "real changes" are the most important ones for analysis.
Here are some examples of real changes at the unit group level:
Creations occur when a new item emerges and is not part of any one or more existing item(s). A deletion, the mirror opposite of creation, occurs when an item expires and no part of it proceeds as part of one or more existing item(s). No new classification items (unit groups) were created to the NOC 2021 Version 1.0, and no NOC 2016 V1.3 classification items were deleted according to the deletion and creation definitions found in GSIM.
Combinations consist of mergers and take-overs among classification items. There were 9 mergers and 3 take-overs of NOC 2016 Version 1.3 unit groups. Essentially, unit groups were combined into an emerging unit group (merger) or an existing unit group (take-over) with the intent to re-arrange the classification of occupational groupings to reflect the current and emerging labour market.
NOC 2016 V1.3 Code(s) | REAL CHANGE GSIM NAME | NOC 2021 V1.0 Code(s) | Description of Change | |||
---|---|---|---|---|---|---|
9222 | Supervisors, electronics manufacturing | Merger | 92021 | Supervisors, electronics and electrical products manufacturing | 9222 and 9223 expired and all parts of both merged into emerging item 92021. | |
9223 | Supervisors, electrical products manufacturing | Merger |
NOC 2016 V1.3 Code(s) | REAL CHANGE GSIM NAME | NOC 2021 V1.0 Code(s) | Description of Change | ||
---|---|---|---|---|---|
9432 | Pulp mill machine operators | Merger | 94121 | Pulp mill, papermaking and finishing machine operators | 9432 and 9433 expired and all parts of both merged into emerging item 94121. |
9433 | Papermaking and finishing machine operators | Merger |
NOC 2016 V1.3 Code(s) | REAL CHANGE GSIM NAME | NOC 2021 V1.0 Code(s) | Description of Change | ||
---|---|---|---|---|---|
7247 | Cable television service and maintenance technicians | Take-over | 72204 | Telecommunications line and cable installers and repairers | 7247 expired and part proceeds between 72204 and 72205. |
Take-over | 72205 | Telecommunications equipment installation and cable television service technicians |
NOC 2016 V1.3 Code(s) | REAL CHANGE GSIM NAME | NOC 2021 V1.0 Code(s) | Description of Change | ||
---|---|---|---|---|---|
9531 | Boat assemblers and inspectors | Take-over | 94219 | Other products assemblers, finishers and inspectors | 9531 expired and all proceeds taken over as part of 94219. |
21 classification items are a result of either a breakdown or a split-off: 10 breakdown and 11 split-offs. Unit groups either expired and were distributed over emerging items (breakdown) or partially continued with part assigned (split-off) to emerging unit group with the intent to better align occupational grouping based on the TEER classification criteria and reflect the current and emerging labour market. Below are examples of decomposition changes related to the addition of more detailed classification items.
NOC 2016 V1.3 Code(s) | REAL CHANGE GSIM NAME | NOC 2021 V1.0 Code(s) | Description of Change | ||
---|---|---|---|---|---|
2263 | Inspectors in public and environmental health and occupational health and safety | Breakdown | 21120 | Public and environmental health and safety professionals | 2263 expired and the denotations distributed among emerging items 21120 and 22232 |
Breakdown | 22232 | Occupational health and safety specialists |
NOC 2016 V1.3 Code(s) | REAL CHANGE GSIM NAME | NOC 2021 V1.0 Code(s) | Description of Change | ||
---|---|---|---|---|---|
0431 | Commissioned police officers | Split-off | 40040 | Commissioned police officers and related occupations in public protection services | 0431 continues as 40040 and part split-off to emerging item 41310. |
Split-off | 41310 | Police investigators and other investigative occupations |
NOC 2016 V1.3 Code(s) | REAL CHANGE GSIM NAME | NOC 2021 V1.0 Code(s) | Description of Change | ||
---|---|---|---|---|---|
1511 | Mail, postal and related workers | Split-off | 64401 | Postal services representatives | 1511 continues as 74100 and part split off to emerging item 64401 |
Split-off | 74100 | Mail and parcel sorters and related occupations |
NOC 2016 V1.3 Code(s) | REAL CHANGE GSIM NAME | NOC 2021 V1.0 Code(s) | Description of Change | ||
---|---|---|---|---|---|
2171 | Information systems analysts and consultants | Split-off | 21211 | Data scientists | 2171 continues as 21222 and part split-off to emerging items 21211, 21220, 21221 and 21233. |
Split-off | 21220 | Cybersecurity specialists | |||
Split-off | 21221 | Business systems specialists | |||
Split-off | 21222 | Information systems specialists | |||
Split-off | 21233 | Web designers |
NOC 2016 V1.3 Code(s) | REAL CHANGE GSIM NAME | NOC 2021 V1.0 Code(s) | Description of Change | ||
---|---|---|---|---|---|
2172 | Database analysts and data administrators | Split-off | 21211 | Data scientists | 2172 continues as 21223 and part split-off to emerging item 21211. |
Split-off | 21223 | Database analysts and data administrators |
NOC 2016 V1.3 Code(s) | REAL CHANGE GSIM NAME | NOC 2021 V1.0 Code(s) | Description of Change | ||
---|---|---|---|---|---|
2173 | Software engineers and designers | Split-off | 21211 | Data scientists | 2173 continues as 21231 and part split off to emerging item 21211. |
Split-off | 21231 | Software engineers and designers |
There were 9 decomposition instances where part of a unit group continued and part split-off to an emerging unit group and part transferred to an existing unit group. Below is an example of this.
NOC 2016 V1.3 Code(s) | REAL CHANGE GSIM NAME | NOC 2021 V1.0 Code(s) | Description of Change | ||
---|---|---|---|---|---|
3124 | Allied primary health practitioners | Split-off | 31302 | Nurse practitioners | 3124 continues as 31303 and part transferred to 32103 and part split off to emerging item 31302. |
Split-off / Transfer | 31303 | Physician assistants, midwives and allied health professionals | |||
Transfer | 32103 | Respiratory therapists, clinical perfusionists and cardiopulmonary technologists |
There were 36 instances where part of a unit group continued and part of it was transferred to one or more existing unit groups with the intent to better align with the TEER classification criteria and in some cases better realign occupational groupings.
NOC 2016 V1.3 Code(s) | REAL CHANGE GSIM NAME | NOC 2021 V1.0 Code(s) | Description of Change | ||
---|---|---|---|---|---|
1253 | Records management technicians | Transfer | 12111 | Health information management occupations | 1253 continues as 12112 and part transferred to 12111. |
Transfer | 12112 | Records management technicians |
NOC 2016 V1.3 Code(s) | REAL CHANGE GSIM NAME | NOC 2021 V1.0 Code(s) | Description of Change | ||
---|---|---|---|---|---|
1525 | Dispatchers | Transfer | 13201 | Production and transportation logistics coordinators | 1525 continues as 14404 and part transferred to 13201. |
Transfer | 14404 | Dispatchers |
Essentially, all items of the classification were affected by virtual changes. For example, all unit groups with 1 to 1 links were still affected by code changes as a result of the TEER system. At the most detailed level of the classification (unit groups) more than 470 unit groups were modified where changes were made to titles and definitions. For example, some titles were modified to better describe the occupational groupings based on content revisions. These types of changes are important for clarification and making necessary updates or corrections.
Finally, all changes made (real or virtual) can potentially have an impact on the content of the classification file, which at the most detailed level contains leading statements, illustrative and example job titles, exclusions, inclusions, main duties, employment requirements and additional information. More than 3500 content items have been either added, deleted or edited and more than 1000 job titles have been added, deleted or edited in this NOC 2021 Version 1.0. These two components of the classification support coding of occupations and fosters fluidity when reading or using the classification.
Level | NOC 2021 V1.0 | NOC 2016 V1.3 | Net Changes | |
---|---|---|---|---|
Broad Category | 10 | 10 | 0 | |
Major Group | 45 | 40 | +5 | |
Sub-major Group | 89Table Note 1 | N\A | +89 | |
Minor Group | 162 | 140 | +22 | |
Unit Group | 516Table Note 2 | 500 | +16 | |
Table Notes
|
For questions related to the National Occupational Classification, please send an email to: statcan.csds-standards-occupations-cnsd-normes-professions.statcan@statcan.gc.ca.
For information on the National Occupational Classification (NOC) and its use for programs and services such as, immigrating to Canada, labour market information, job searches and working in Canada, please contact Employment and Social Development Canada (ESDC).
Release date: October 25, 2023
Invitation to participate in the revision of the National Occupational Classification (NOC) Updated on: October 3, 2024
This standard was approved as a departmental standard on August 10th, 2021.
The publication of the National Occupational Classification (NOC) 2021 is the thirtieth anniversary of the standard occupational classification system and it introduces a major structural change. The NOC 2021 Version 1.0 overhauls the "Skill Level" structure by introducing a new categorization representing the degree of Training, Education, Experience and Responsibilities (TEER) required for an occupation. The NOC 2021 Version 1.0 also introduces a new 5-digit hierarchical structure, compared to a 4-digit hierarchical structure in the previous versions of the classification. The NOC has been developed and maintained as part of a collaborative partnership between Employment and Social Development Canada and Statistics Canada. This revision is extensive; the last structural revision was NOC 2011.
Date: August, 2021
Program manager: Director, Centre for Population Health Division
Director General, Health, Justice, Diversity and Populations
Personal information collected through the Survey on Health Care Workers' Experiences During the Pandemic is described in Statistics Canada's "Health Surveys" Personal Information Bank. The Personal Information Bank refers to personal information that is related to participants of health surveys conducted by Statistics Canada. The personal information may include the following: name, contact, biographical, biometric, citizenship status, education, employment, financial, language, health and medical information (from blood, urine and hair samples), pregnancy, breastfeeding, sleep habits, sexual behaviour, nutrition, alcohol and e-cigarette/cigarette use, medication/drug use, physical attributes, physical activity, neighbourhood environment, place of birth, and provincial health card number.
The "Health Surveys" Personal Information Bank (Bank number: StatCan PPU 806) is published on the Statistics Canada website under the latest Information about programs and Information Holdings chapter.
Statistics Canada is conducting the Survey on Health Care Workers' Experiences During the Pandemic, under the authority of the Statistics Act Footnote 1 , on behalf of the Public Health Agency of Canada and Health Canada.
The purpose of this survey is to understand the impact of the COVID-19 pandemic on health care workers in Canada. This voluntary survey will cover topics such as job type and setting, personal protective equipment (PPE) and infection prevention and control (IPC) practices and protocols, COVID-19 vaccination and diagnosis, and the impacts of the pandemic on personal health and work life. It also includes general demographic questions.
A master microdata file will be produced and made available in Statistics Canada's Research Data Centres (RDC)Footnote 2 . A subset of the master file which contains only information of respondents who have consented to share their information, called the share file, will be made available to the Public Health Agency of Canada (PHAC) Health Canada (HC), the Institut de la Statistique du Québec (ISQ) and provincial and territorial ministries of health. A Public Use Microdata File (PUMF)Footnote 3 will also be produced for use by the Canadian Institute for Health Information (CIHI) and will be available to members of the public. Health Canada, PHAC and CIHI plan to use the survey results to help inform health care workforce planning, the delivery of health care services and to better understand what health care workers need in terms of equipment, training and support.
The survey targets health care workers and those working in a health care setting in Canada since the start of the COVID-19 pandemic who are living in the 10 provinces. Because of the small number of health care workers in the territories, the limitation of collection to the capital cities and the low response rates currently being experienced in the territories it was deemed that collection in the territories would not produce enough respondents to be able to release reliable estimates at the individual territorial level.
Questions on health include self-perceived health, mental health, and daily stress both currently and compared to before the COVID-19 pandemic, resilience, feelings of anxiety, depression, or suicide, chronic conditions, and changes in lifestyle and behaviours, such as consumption of alcohol, tobacco, cannabis, pain relievers for non-therapeutic purposes, or illegal drugs.
Questions on COVID-19 include the impact of COVID-19 on the respondent's job and personal life, COVID-19 diagnosis, possible hospitalization, where it was contracted, and reason(s) for getting tested. Also included are questions on COVID-19 vaccination, reasons for not having been vaccinated (if applicable), and precautions taken at home related to COVID-19.
In addition, respondents will be asked to confirm their name, and provide other demographic information such as date of birth, age, gender, postal code, province/territory of residence, province/territory of work, Indigenous identity, population group, immigration and citizenship and income. The purpose of including these questions is to determine if there are differences in the impacts of the pandemic on health care workers from various groups. For example, those living in different provinces, younger or older health care workers, or differences among genders.
A sample of 32,500 individuals were selected from Census 2016 long form respondents. These were individuals who had identified as a health care worker at the time of Census 2016 or who were registered in a health care education program between 2015 and 2018, according to the Postsecondary Student Information System (PSIS). This is a targeted respondent survey. Responses will be aggregated to ensure that no individuals can be directly or indirectly identified.
While the Generic Privacy Impact Assessment (PIA) addresses most of the privacy and security risks related to statistical activities conducted by Statistics Canada, this supplement describes additional measures being implemented due to the sensitivity of the information being collected. As is the case with all PIAs, Statistics Canada's privacy framework ensures that elements of privacy protection and privacy controls are documented and applied. The Survey on Health Care Workers' Experiences During the Pandemic will collect information on the impact of the COVID-19 pandemic on health care workers' personal and professional lives, including their mental well-being (including feelings of anxiety, depression, or suicide) as well as sensitive personal information such as name, date of birth, and gender identity. This supplement describes how Statistics Canada designed and developed this survey while taking into account the possible impact on vulnerable populations.
The collection and use of personal information for the Survey on Health Care Workers' Experiences During the Pandemic can be justified against Statistics Canada's Necessity and Proportionality Framework:
The impact of the COVID-19 pandemic on health care workers is not fully understood. There are few existing sources of information on this topic, in particular on the impact on health care workers' personal life, so a survey is needed to fill this data gap. The results of this survey may be used by the Public Health Agency of Canada, Health Canada, the Canadian Institute for Health Information and other government organizations to help to inform health care workforce planning, the delivery of health care services and to better understand what health care workers need in terms of equipment, training and support.
Only health care workers living in the provinces are eligible to participate. This is a targeted respondent survey and respondents will confirm that they are health care workers at the start of the survey. The demographic data and occupational group information collected will be used for analysis of subgroups of the population. The health care workers have been stratified into four groups from a list of 24 occupations: physicians, nurses, personal support workers (PSWs) and other health care workers. A goal of the survey is to be able to understand how the impacts of the pandemic are affecting different types of health care workers.
The survey data file, without direct identifiers other than postal code and date of birth, will be made available to researchers in the Research Data Centres (RDC) upon approval of requests to access the data for statistical research. Statistics Canada's directives and policies on data publication will be followed to ensure the confidentiality of any data released from the RDC. Only aggregate results, which are fully anonymized and non-confidential, without direct identifiers, which precludes the possibility of re-identifying individuals, can be released from the RDC. Statistics Canada will retain this data as long as required for statistical purposes, in order to conduct analysis of long‐term impacts.
Although there are currently no plans for record linkage, direct personal identifiers such as name will be retained on a separate file in a secure location for potential linkage opportunities in the future.
Statistics Canada's microdata linkage and related statistical activities were assessed in Statistics Canada's Generic Privacy Impact Assessment.Footnote 4 All data linkage activities are subject to established governanceFootnote 5 , and are assessed against the privacy principles of necessity and proportionality.Footnote 6 All approved linkages are published on Statistics Canada's website.Footnote 7
A questionnaire was developed by following Statistics Canada's processes and methodology to produce results that are representative of the population. The survey will be administered using a self-reported electronic questionnaire with interviewer telephone follow-up for non-response. A random sample of health care workers or individuals expected to be working as health care workers from Statistics Canada's 2016 Census 2016 will receive an invitation letter and secure access code to complete the survey on Statistics Canada's secure website. After 3 and a half weeks, interviewers will follow up with individuals that have not yet responded, to re-issue the invitation and provide respondents with the opportunity to complete the survey over the telephone with a trained Statistics Canada interviewer. The collection period will be approximately ten weeks. All Statistics Canada directives and policies for the development, collection, and dissemination of the survey will be followed, and survey responses will not be attached to respondents' addresses or phone numbers. The data will be representative of the health care worker population and may be disaggregated by province, ethnicity, gender, age groupings, and other variables; in order to ensure anonymity.
Data on mental health and its impacts, as well as data on consumption of illegal drugs are highly sensitive. Moreover, mental-health issues may be exacerbated due to COVID‐19 isolation protocols. For these reasons, experts at Statistics Canada have been consulted on the scope and methodology of the survey. Wherever possible, questions about mental health and well‐being from existing surveys have been used. Some of these questions were taken from the Survey of COVID-19 and Mental Health (SCMH) and have previously undergone qualitative testing; the SHCWEP questionnaire also underwent qualitative testing.
All the data to be collected are required to fulfill the purpose of the survey as described above. All questions and response categories were carefully considered to ensure they accurately capture the data in question to help inform activities such as health care workforce planning, the delivery of health care services and to better understand what health care workers need in terms of equipment, training, and support.
Statistics Canada directives and policies with respect to data collection and publication will be followed to ensure the confidentiality of the data. Individual responses will be grouped with those of others when reporting results. Individual responses and results for small groups (as established by minimum prevalence levels for each variable among these small groups) will not be published or shared with government departments or agencies. This approach will also reduce any potential impact on vulnerable populations or subsets of populations, as the grouping of results will protect the confidentiality of individuals within a particular subset of the population. As permitted by the Statistics ActFootnote 8 and with consent of individual respondents, survey responses may be shared with the Public Health Agency of Canada, Health Canada, provincial and territorial ministries of health, and for Quebec residents, the Institut de la statistique du Québec, strictly for statistical and research purposes, to aid in future policy decisions related to health care workers, in accordance with Statistics Canada's security and confidentiality requirements.
The findings will support decision-making at all levels of government and improve knowledge and understanding of the impact of the COVID-19 pandemic on health care workers, and will help inform government decision‐making in order to support health care workers in their personal and professional lives. The privacy measures taken are proportional to the potential risks to an individual's privacy.
Proportionality has also been considered based on ethics:
Prior to collection, individuals selected to participate in the survey will be clearly informed that the survey is voluntary. They will also be informed of the survey's purpose and topics, so that they can make an informed decision about whether they want to participate. This notification to all potential participants will be done in writing on the questionnaire, or verbally by the interviewer before any questions are asked. They will also be asked if their data can be shared with the Public Health Agency of Canada, Health Canada, provincial and territorial ministries of health, and for Quebec residents, the Institut de la statistique du Québec.
Since some of the survey questions are sensitive and could lead to distress, mental-health resources will be included in the help text for those questions, which can be accessed in the electronic questionnaire and during interviews.
The help text reads as follows and includes 10 resources of which 3 are listed here as examples:
In the current context of COVID-19, many people are trying to adjust to the new norms, such as returning to work or day-to-day life. During this time, many people may not feel that they are in control of things, and it is normal to feel concerned, sad, stressed, confused, scared or worried.
Should you need any support, please contact any of the following resources:
Canada Suicide Prevention Service
A national network of existing distress, crisis and suicide prevention line services
Crisis Services Canada website
Telephone: 1-833-456-4566
APPELLE (Quebec Residents)
Help line for those thinking about suicide or are worried about a loved one
Telephone: 1-866-277-3553
Centre for Addiction and Mental Health
A wide range of clinical care services for mental illness and addictions
Telephone: 1-800-463-2338
Research was conducted on existing administrative data and other surveys related to health care workers. It was determined that these types of data sources would not provide the details needed to fully understand the impact of the COVID-19 pandemic on health care workers. As a result, it was determined that a survey to collect this information was required. A previous crowdsource collection, where information is collected from volunteers, took place in November and December of 2020 which collected information primarily related to the participant's work environment and access to infection prevention and control (IPC) and personal protective equipment (PPE) at work without much information about individuals' personal lives. Additionally, because it was a crowdsource collection, it is not possible to use it to produce estimates that are representative of the Canadian population. Based on discussions between health and methodology experts within Statistics Canada and the Public Health Agency of Canada, it was determined that a survey with at least 30,000 units was necessary to produce reliable and accurate results by province and the four health occupation groups of interest (physicians, nurses, PSWs, and other health care workers). Releasing data at these aggregated levels would reduce the potential to identify impacts on vulnerable populations, subsets of populations, and groups.
Some questions contained in the Survey on Health Care Worker's Experiences During the Pandemic are considered sensitive as they relate to an individual's mental health and well-being. The overall risk of harm to the survey respondents has been deemed manageable with existing Statistics Canada safeguards that are described in Statistics Canada's Generic Privacy Impact Assessment, as well as with the following measures:
Mental-Health Resources:
As with mental health surveys conducted by Statistics Canada, mental-health resources and contact information will be provided to respondents as a help button within the electronic questionnaire. In addition, in the case of telephone follow-up for non-response, interviewers will be trained and equipped to offer mental health resources and contact information to survey respondents.
Transparency:
Prior to collection, individuals selected to participate in the survey will be clearly informed that the survey is voluntary. They will also be informed of the survey's purpose and topics, so that they can make an informed decision about whether they want to participate. This notification to all potential participants will be done in writing on the questionnaire, or verbally by the interviewer before any questions are asked. The topics listed as part of the survey will include: job type and setting, personal protective equipment (PPE) and infection prevention and control (IPC) practices and protocols, COVID-19 vaccination and diagnosis, and the impacts of the pandemic on personal health and work life. It also includes general demographic questions. This information will be provided through invitation and reminder letters, and will be repeated at the beginning of the questionnaire. Information about the survey, as well as the survey questionnaire, will also be available on Statistics Canada's website.
Confidentiality:
Individual responses will be grouped with those of others when reporting results. Individual responses and results for very small groups will never be published or shared with government departments or agencies. Following careful analysis of the data, consideration will be given prior to the release of aggregate data to ensure that marginalized and vulnerable communities are not disproportionally impacted. As permitted by the Statistics Act, and only with the consent of the respondent, survey responses may be shared with PHAC, Health Canada and provincial and territorial ministries of health, strictly for statistical and research purposes, and in accordance Statistics Canada's security and confidentiality requirements. The postal code will not be used to identify respondents given that only aggregated data will be released.
This assessment concludes that with the existing Statistics Canada safeguards and additional mitigation factors listed above, any remaining risks are such that Statistics Canada is prepared to accept and manage the risk.
Geography | Month | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
202007 | 202008 | 202009 | 202010 | 202011 | 202012 | 202101 | 202102 | 202103 | 202104 | 202105 | 202106 | 202107 | |
percentage | |||||||||||||
Canada | 0.7 | 0.7 | 0.7 | 0.5 | 0.6 | 0.8 | 0.8 | 0.7 | 0.6 | 0.7 | 0.9 | 0.8 | 0.6 |
Newfoundland and Labrador | 0.2 | 0.4 | 0.4 | 0.4 | 0.4 | 0.4 | 0.6 | 0.5 | 0.2 | 1.2 | 2.3 | 0.3 | 0.3 |
Prince Edward Island | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Nova Scotia | 2.1 | 1.9 | 1.7 | 2.7 | 3.4 | 6.3 | 1.8 | 1.7 | 2.6 | 4.8 | 8.1 | 3.0 | 1.6 |
New Brunswick | 2.0 | 3.6 | 3.5 | 2.9 | 5.0 | 3.5 | 3.4 | 2.6 | 1.1 | 1.1 | 1.9 | 3.4 | 2.3 |
Quebec | 1.7 | 2.3 | 1.9 | 1.5 | 1.4 | 1.7 | 1.8 | 1.8 | 1.9 | 1.8 | 3.1 | 2.9 | 1.6 |
Ontario | 1.0 | 0.9 | 1.0 | 0.8 | 0.9 | 1.3 | 1.2 | 1.1 | 0.9 | 1.1 | 1.2 | 0.9 | 0.8 |
Manitoba | 1.2 | 1.8 | 2.8 | 1.7 | 1.4 | 2.5 | 1.7 | 2.4 | 1.8 | 2.8 | 5.3 | 1.7 | 0.8 |
Saskatchewan | 1.2 | 1.4 | 0.7 | 0.9 | 0.9 | 1.0 | 1.0 | 1.6 | 1.2 | 0.8 | 0.7 | 0.8 | 0.7 |
Alberta | 2.3 | 1.9 | 3.4 | 1.3 | 1.3 | 1.7 | 1.0 | 1.2 | 1.1 | 1.2 | 1.4 | 1.2 | 1.5 |
British Columbia | 1.3 | 1.9 | 1.8 | 1.4 | 1.5 | 1.4 | 1.5 | 1.4 | 1.5 | 1.3 | 1.4 | 1.4 | 1.5 |
Yukon Territory | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Northwest Territories | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Nunavut | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
June 15, 2021, 8:30 a.m. to 12:30 p.m.
Virtual meeting via Zoom
Jacques Fauteux, André Loranger, Greg Peterson, Lynn Barr-Telford, Martin St-Yves, Leila Boussaïd, Eric Rancourt, Tom Dufour, Janique Godin, Janice Keenan, Jeff Latimer, Nathalie Brault, Li Xue, Sevgui Erman, Susie Fortier, Yanick Beaucage, Keven Bosa, Martin Beaulieu, Geneviève Jourdain, Marc St-Denis, Farnaz Ahanin, Alexa Tupy
Anil Arora, Chief Statistician of Canada
Mr. Arora opened the meeting by welcoming members to the fourth biannual meeting of the Advisory Council on Ethics and Modernization of Microdata Access (ACEMMA), and thanking them for their continuous advice and guidance as the agency continues to modernize. He noted that their skills and expertise are of utmost importance to Statistics Canada (StatCan), and are essential as the agency continues to determine how best to serve Canadians with the highest ethical standards and in a responsible manner when dealing with sensitive data sources, while delivering de-identified microdata access to researchers and policy makers that benefits all Canadians.
A recap of the previous meeting was provided. Mr. Arora highlighted ongoing efforts to integrate ethical frameworks and effective governance within all the inputs and outputs of the agency in a systematic and focused manner with transparency to Canadians. He also noted the continued advancements of creating new and innovative methods for collaboration, enhancing the agency's statistical programs, producing more disaggregated data, and making data more open to the research community while adhering to confidentiality requirements and governing frameworks. Additionally, various StatCan ongoing key initiatives were outlined, including developments of the Virtual Data Lab, Virtual Research Data Centre, Client Relationship Management System, accreditation frameworks, and the Confidentiality Classification Tool (CCT) scoring of additional datasets.
Leila Boussaïd, Director General, Data Access and Dissemination Branch, Statistics Canada
Marc St-Denis, Assistant-director, Data Access and Dissemination Branch, Statistics Canada
Ms. Boussaïd shared updates on the Virtual Data Lab (vDL) project. As the vDL will go into production in October 2021, the goal remains to provide increased access to accredited researchers through the use of authorized workspaces and remote capabilities. She noted that the Protected B Cloud environment should be available for all levels of government, non-profit organizations, and academia, and will provide 24/7 remote access up to CCT 7 score data. Pilots are presently being conducted to gather lessons learned and to work towards an overall improved end-state of the environment. Mr. St-Denis addressed the project's plan and timelines regarding the transition of all current existing partners to the vDL Cloud environment.
Council members expressed support and enthusiasm for the project, and agreed that advancements in technology allow to achieve greater access to data in a secure and virtual way. Members inquired about the security approach for this project, wanting to ensure that both external and internal threats were addressed. A discussion took place on options to mitigate risks such as accreditation and real time monitoring.
Eric Rancourt, Director General, Modern Statistical Methods and Data Science Branch
Greg Peterson, Assistant Chief Statistician, Economics Statistics Field
Janice Keenan, Director General, Communications Branch
Jacques Fauteux, Assistant Chief Statistician, Strategic Engagement Field, Statistics Canada (Moderator)
Mr. Rancourt shared progress with the Office of Privacy Commissioner (OPC) and accomplishments that have been achieved for data ethics and privacy at StatCan, such as the creation of an internal Data Ethics Committee and Secretariat, the increased execution of data ethics reviews, development of a Framework for Responsible Machine Learning, work to build a data sensitivity scale that will better inform managers about their programs, enhancement to the Necessity and Proportionality Framework, and availability of training in Data Ethics. Mr. Rancourt emphasized the importance of identifying potential social biases, stigmatization and data sensitivity, and asked council members the following questions:
Mr. Peterson led the discussion on the acquisition of data. He indicated that the pandemic has brought forth new data requirements and he highlighted the following three: (1) housing prices and implications on citizens; (2) equitable recovery and growth, and; (3) inflation and the acquisition of better information on consumer credit. Mr. Peterson highlighted the engagement work that is being done to continue to build relationships with various stakeholders to determine how best to serve their needs and collaborate with them on data acquisition.
Ms. Keenan led the discussion on the Trust Agenda and social license. A number of initiatives are underway, including the creation of a Communications and Engagement Strategy, enhancement of the agency's Trust Centre to make it more interactive, and the development of enhanced social acceptability metrics. Ms. Keenan emphasized the importance of continued engagement with the Canadian public to enable them to better understand the impact and relevance of StatCan data in their daily lives and their communities.
Council members agreed that there is a need to communicate and engage with Canadians to inform them, and asked what the agency can do to promote the work of StatCan and connect with people who do not visit the website. An extensive discussion on social license took place. Members suggested that partnerships could help improve understanding of the work of StatCan and can help mitigate concerns of some stakeholders. Members would like to see StatCan showcase examples of when society has received helpful information as a result of the agency's statistics, and to continue building on the notion of trust. Mr. Arora thanked the members for their input. He requested if the committee would take on the role of advising on the agency's state of readiness, as there is a desire for the committee to provide guidance regarding the agency moving forward and reasonable next steps in its initiatives. Members unanimously agreed to take on the role.
Nathalie Brault, Director, Centre for Special Business Projects, Statistics Canada
Li Xue, Director, Social Analysis and Modelling Division, Statistics Canada
Jeff Latimer, Director General, Health, Justice, Diversity and Populations Branch, Statistics Canada
Lynn Barr-Telford, Assistant Chief Statistician, Social, Health and Labour Statistics, Statistics Canada (Moderator)
Mr. Latimer began the discussion with a presentation on the intended approach for the agency's Disaggregated Data Action Plan that was recently announced in the 2021 Budget. The plan will focus on contributing to a more equitable Canada by collecting, analyzing, and disseminating disaggregated data to improve insights and decision-making, and support more representative data collection and enhance statistics on diverse populations. Mr. Latimer noted that to achieve the plan's objective, StatCan is engaging with government partners and communities as part of its efforts to maintain social licence, working collaboratively with key stakeholders to collect and acquire new data, and improving access to the data and insights.
Ms. Brault continued the presentation in discussing disaggregated data in a business context. COVID-19 was a driver for increased demand and use of disaggregated data. She touched on the Canadian Survey on Business Conditions, launched in summer 2020. The survey collects information on businesses to identify potential emerging issues, to maintain a regular pulse on business intentions and sentiment and, in the short term, assess economic recovery. Ms. Brault noted that the agency is currently working with organizations such as Chamber of Commerce, the Canadian Business Resilience Network, as well as Black North to ensure that our products and services continue to be relevant to as many Canadians as possible.
Ms. Xue concluded the presentation noting that the goal is to enhance disaggregated data and facilitate research on complex issues and intersectionality. This will be achieved through the innovative approaches to disaggregated data and information (i.e. modeling, geomatics), and engaging with partners on collaborative pilot projects.
Council members were invited to provide input on the following questions for consideration:
Mr. Arora noted the important role that the agency can continue to play as a data steward and the importance to lead by example. Standards will also play a key role in enabling a secure infrastructure with tools that build trust and capacity.
Council members noted the importance to make the distinction between the use of disaggregated data for policy making and for informing Canadians. With disaggregated data, more information will become available regarding which populations are underprivileged and the disproportionate impact on some groups in society. Members cautioned that the information can be accompanied with complicated ethical questions and issues, however, that the benefits are bigger than the drawbacks and that communicating these benefits and intentions with the public and how we engage will be key. There was general support for the strategic direction being pursued on the Disaggregated Data Action Plan (DDAP).
Sevgui Erman, Director, Data Science Division, Statistics Canada
Yanick Beaucage, Chief, Data Science Division, Statistics Canada
Keven Bosa, Chief, Data Science Division, Statistics Canada
Susie Fortier, Director, International Cooperation and Methodology Innovation Centre, Statistics Canada
Martin Beaulieu, Chief, International Cooperation and Methodology Innovation Centre (Data Ethics Secretariat), Statistics Canada
André Loranger, Assistant Chief Statistician, Strategic Data Management, Methods and Analysis, Statistics Canada (Moderator)
Mr. Loranger noted that the Data Science Division and the Data Science Network had been created to help conduct ethical reviews and further advance data science techniques at StatCan. Mr. Bosa continued the discussion with a presentation on behalf of the group. He highlighted that as the use of artificial intelligence (AI) and machine learning methods continues to expand, there is a need for a framework and review process of machine learning at StatCan. Having a framework and review process preserves confidentiality, ensures transparency, maintains Canadian trust in StatCan as their data stewards, and helps produce statistics of quality. Mr. Bosa noted that the agency created a Framework for Responsible Machine Learning Processes that provides guidelines for the evaluation of applications for all statistical programs and projects using machine learning algorithms. The framework focuses on the following four themes: respect for people, respect for data, sound application, and sound methods. Mr. Bosa concluded the presentation and invited council members to provide input on the following questions for consideration:
Council members agreed with the importance of all four components of the Framework, and noted that it is a tool that can be useful and informative and help build credibility into the agency's overall process. Members cautioned that the application of new AI technologies have ethical implications, such as potential issues of algorithmic bias. To address potential ethical implications, it is crucial for the agency to carefully review the data elements being used. Regarding social buy-in, members noted that remaining transparent will be key.
Anil Arora, Chief Statistician of Canada
Mr. Arora thanked members and invited them to share their final thoughts.