Canadian Nurses’ Psychological Health Assessment and Its Determinants During the Uncertain Context of the Early COVID-19 Pandemic: A Cross-Sectional Study
Bibliographic record
Abstract
Quebec's (Canada) nurses experienced a major reorganization of care during the first wave of the COVID-19 pandemic. This study aimed at investigating nurses’ psychological health and its determinants during this highly uncertain time, with a particular focus on posttraumatic stress disorder (PTSD), anxiety, and depression. In 2020, a web-based cross-sectional survey was completed by a large sample of Quebec nurses ( n = 1,773). High prevalence of PTSD (14.3%), anxiety (39.4%), and depression (46.7%) was observed. Overcommitment at work was associated with higher prevalence of PTSD, anxiety, and depression. More years since licensure, feeling safe with protective measures and increased social support were associated with lower prevalence of PTSD, anxiety, and/or depression. Our study identified modifiable personal and workplace factors that could be targeted by healthcare organizations and policymakers to promote nurses’ well-being and enhance the resilience of healthcare systems to resist future global health crises or pandemics. Future research is needed to better understand the potential long-term consequences of the COVID-19 pandemic on nurses’ psychological health.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".