Psychological distress, depression symptoms and fatigue among Quebec nursing staff during the COVID‐19 pandemic: A cross‐sectional study
Bibliographic record
Abstract
AIM: To describe the state of health of Quebec nursing staff during the pandemic according to their exposure to COVID-19, work-related characteristics and sociodemographic factors (gender, generational age group). State of health was captured essentially by assessing psychological distress, depression symptoms and fatigue. DESIGN AND METHODS: A large-scale cross-sectional study was conducted with 1,708 nurses and licenced practical nurses in Quebec (87% women, mean age of 41 ± 11 years). The survey included several questionnaires and validated health-related scales (psychological distress, depression symptoms and fatigue). The STROBE guidelines were followed in reporting the study's findings. RESULTS: Results showed that the prevalence of psychological distress and depression symptoms was moderate to severe. Women, generation Xers and Yers, nurses who cared for COVID-19 patients and those with a colleague who was infected with COVID-19 at work scored higher for fatigue, psychological distress and depression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".