Well-being of health workers during the COVID-19 pandemic in Quebec, Canada
Bibliographic record
Abstract
Patient care is closely tied to provider well-being. While health workers’ burnout, stress, mental health, anxiety, and depression have been a concern for over a decade, it has only become at the centre of attention since the COVID-19 pandemic. There is growing evidence on prevalence and factors of various dimensions of well-being. However, most studies focus on specific types of health workers, which does not enable comparison and understanding of potential differences between types of workers. The study aims to fill this gap by examining the well-being of health workers in the fall of 2021, during the COVID-19 pandemic in Quebec, Canada, with stratification by type of health worker and on different dimensions of well-being. We used data from the Survey on Health Care Workers' Experiences During the Pandemic, conducted by Statistics Canada between September 2, 2021, and November 12, 2021. Our findings show rates of poor mental health, anxiety, and depression of 38 %, 21 % and 13 % respectively. Emotional distress was associated with anxiety, depression and poor mental health for all types of health workers. Having a health issue was associated with anxiety for all types of health workers. Experiencing stigma was associated with poor mental health for all types of health care workers, while for anxiety and depression, it was significant for the study population as a whole, but not for each group in stratified analyses. Conflicts between colleagues were associated with poor mental health across all types of health workers. Prevalence of anxiety, depression and poor mental health are high among health workers. Although there are common factors, there are also some specificities by type of worker, which suggests that strategies need to be customized to address the different dimensions and improve the well-being of health workers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".