Evolution of stressors for healthcare workers over the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Many studies have documented exposure to psychosocial factors in healthcare workers (HCWs) during the coronavirus disease 2019 (COVID-19) pandemic. Few have done it prospectively with open-ended questions. AIMS: To describe the evolution of stress factors reported by HCWs during the pandemic. METHODS: A prospective cohort of 4964 HCWs (physicians, nurses, healthcare aides and personal support workers), from four Canadian provinces, received four online questionnaires between the spring 2020 and 2022. Responses to an open-ended question on stressful events were coded into 25 stressors. Multilevel logistic regressions assessed trends in stressors, and the effect of gender, occupation and age. RESULTS: In 2020, 91% of participants reported at least one stressor compared to 51% in spring 2022. Overall, eight stressors were reported 1000 times or more among 16 786 questionnaires. Five stressors decreased over time: fear of COVID-19, problems with personal protective equipment, changing guidelines, management of difficult cases and changes to work routine. Conversely, an increasing trend was noted for volume of work, and poor behaviour from the public or colleagues. Difficulties managing patients' deaths remained steady. Changes in stressors over time were similar by gender, professional role and age group, although the reporting of some stressors varied within subgroups. CONCLUSIONS: Reporting of most stressors decreased over the pandemic, except for stressors arising from the changing demands on staff and patients as the pandemic progressed. Such changes in workplace psychosocial factors need to be addressed by prevention programmes to ensure an appropriate support response to the needs of HCWs.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".