Evolution of burnout and psychological distress in healthcare workers during the COVID-19 pandemic: a 1-year observational study
Bibliographic record
Abstract
BACKGROUND: Long-term psychological impacts of the COVID-19 pandemic on healthcare workers remain unknown. We aimed to determine the one-year progression of burnout and mental health since pandemic onset, and verify if protective factors against psychological distress at the beginning of the COVID-19 pandemic (Cyr et al. in Front Psychiatry; 2021) remained associated when assessed several months later. METHODS: We used validated questionnaires (Maslach Burnout Inventory, Hospital Anxiety and Depression and posttraumatic stress disorder [PTSD] Checklist for DSM-5 scales) to assess burnout and psychological distress in 410 healthcare workers from Quebec, Canada, at three and 12 months after pandemic onset. We then performed multivariable regression analyses to identify protective factors of burnout and mental health at 12 months. As the equivalent regression analyses at three months post-pandemic onset had already been conducted in the previous paper, we could compare the protective factors at both time points. RESULTS: Prevalence of burnout and anxiety were similar at three and 12 months (52% vs. 51%, p = 0.66; 23% vs. 23%, p = 0.91), while PTSD (23% vs. 11%, p < 0.0001) and depression (11% vs. 6%, p = 0.001) decreased significantly over time. Higher resilience was associated with a lower probability of all outcomes at both time points. Perceived organizational support remained significantly associated with a reduced risk of burnout at 12 months. Social support emerged as a protective factor against burnout at 12 months and persisted over time for studied PTSD, anxiety, and depression. CONCLUSIONS: Healthcare workers' occupational and mental health stabilized or improved between three and 12 months after the pandemic onset. The predominant protective factors against burnout remained resilience and perceived organizational support. For PTSD, anxiety and depression, resilience and social support were important factors over time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".