Impacts of COVID-19 on Health Care Workers: Infection Prevention and Control (ICHCWIPC)

Detailed information for November to December 2020




One Time

Record number:


The purpose of this crowdsource questionnaire is to understand the impacts of COVID-19 on Canadian health care workers, with particular focus on access to personal protective equipment (PPE) and infection prevention and control (IPC) measures in the workplace.

Data release - February 2, 2021


This questionnaire covers job type and setting, training and information on PPE and IPC practices and protocols, use and access to PPE, and personal health. It also includes general demographic questions.

Information collected may be used by the Public Health Agency of Canada, Health Canada, the Canadian Institute for Health Information and other government organizations to inform the delivery of health care services, and to ensure health care workers have the equipment, training and support they need to do their job.


  • Health
  • Health care services
  • Labour
  • Mental health and well-being
  • Workplace organization, innovation, performance

Data sources and methodology

Target population

Health care workers and those working in a health care setting living in the ten provinces and three territories are eligible to participate. This includes people who provide health care services directly to individuals (e.g. physicians, nurses, massage therapists, dentists, dietitians), provide technical support to medical staff (e.g. receptionists, technicians), or who provide support services in a health care setting (e.g. cleaning and food services staff, security).

Instrument design

The content for the Impacts of COVID-19 on Health Care Workers: Infection Prevention and Control electronic questionnaire was drafted in consultation with the Public Health Agency of Canada, Health Canada and the Canadian Institute for Health Information.

The questionnaire follows standard practices and wording used in a computer-assisted interviewing environment, such as the automatic control of flows that depend upon answers to earlier questions and the use of edits to check for logical inconsistencies and capture errors. The computer application for data collection was tested extensively.


No sample selection is done for this crowdsourcing initiative.

Data sources

Data collection for this reference period: November 24, 2020 to December 13, 2020

Responding to this survey is voluntary.

Data are collected directly from survey respondents.

View the Questionnaire(s) and reporting guide(s) .

Error detection

An edit on age was built into the online questionnaire to maximize the quality of the data. Further error detection of invalid or missing values was performed at a micro level on the received data for age, gender, population group, and postal code.


Valid values of the postal code (or its first three digits or first digit in the absence of a complete postal code) were used to derive a province of residence, and a Census Metropolitan Area (CMA) when possible.
If participants answered employment related questions but EMP_Q30 was blank, this variable was set to 1 (yes).

No other imputation was performed on questions left unanswered by participants.


Because of the non-probabilistic nature of the crowdsourcing data collection, a probability of selection was not available given the absence of a sample design and a survey weight was not calculated. Furthermore, no adjustment was made for non-response as the concept of a non-response rate is not applicable in the crowdsourcing context.

Estimates from the 2016 Census on the number of people working in health care occupations or in a health care setting by province were used to calculate a benchmarking factor for every participant to compensate for over/underrepresentation. Those benchmarking factors should be used to produce results in the same way weights are used to produce estimates from a probabilistic survey. However, because of the non-probabilistic nature of crowdsourcing, results should be limited to proportions and totals should not be produced. Furthermore, benchmarking factors should not be used to calculate measures of precision (coefficients of variation, margins of error, confidence intervals).

Quality evaluation

While an outlier detection process is used on the crowdsourced data, caution must be exercised when interpreting these data because data collection is conducted on a voluntary basis and therefore subject to multiple biases.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada Generalized Disclosure Control System (G-Confid). G-Confid is used for primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.

Revisions and seasonal adjustment

This methodology type does not apply to this survey.

Data accuracy

This methodology does not apply.

Benchmarking factors should not be used to calculate measures of precision (coefficients of variation, margins of error, confidence intervals).

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