A longitudinal study of hospital workers’ mental health from fall 2020 to the end of the COVID-19 pandemic in 2023
Bibliographic record
Abstract
Most longitudinal studies of healthcare workers' mental health during COVID-19 end in 2021. We examined trends in hospital workers eight times, ending in 2023. A cohort of healthcare workers at one organization was surveyed at 3-month intervals until Spring 2022 and re-surveyed in Spring 2023 using validated measures of common mental health problems. Of 538 workers in the original cohort, 289 (54%) completed the eighth survey. Repeated-measures ANOVA revealed significant changes in psychological distress (F = 7.4, P < .001), posttraumatic symptoms (F = 14.1, P < .001), and three dimensions of burnout: emotional exhaustion (F = 5.7, P < .001), depersonalization (F = 2.7, P = .01), and personal accomplishment (F = 2.8, P = .008). Over time, psychological distress and depersonalization increased, posttraumatic symptoms and personal accomplishment decreased, and emotional exhaustion fluctuated significantly without net change. Most measures did not improve significantly in the year prior to the declaration of the pandemic's end. The lack of improvement in psychological distress, emotional exhaustion, depersonalization, and personal accomplishment during the period in which COVID-19 case rates declined and public health measures were relaxed is a concerning indication of the chronicity of the impact of the pandemic on healthcare 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".