Psychological Impacts of the COVID-19 Pandemic on Canadian Healthcare Workers
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to describe mental health impacts of the COVID-19 pandemic and identify roles that predict distress among Canadian healthcare workers (HCWs). METHODS: Using data from three cross-sectional Canadian surveys, we compared 799 HCWs to demographically matched controls and compared HCWs with and without COVID-19 patient contact. Participants completed validated measures of depression, anxiety, trauma-related stress, alcohol problems, coping self-efficacy, and sleep quality. RESULTS: Non-HCWs reported more depression and anxiety in Fall 2020 and more alcohol problems in Fall/Winter 2021 than HCWs. In Winter 2020-2021, HCWs reported more trauma-related stress than non-HCWs. As of early 2021, HCWs with direct patient contact reported worse symptoms across nearly all measures than HCWs without. CONCLUSIONS: Although Canadian HCWs did not report worse mental health than demographically similar peers, mental health supports are needed for HCWs providing direct patient care.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".