Trusting in times of the COVID-19 crisis: Workplace and government trust and depressive symptoms among healthcare workers
Bibliographic record
Abstract
Previous research has highlighted the negative impact of the COVID-19 pandemic on healthcare workers' (HCWs) mental health, yet protective factors remain underexplored. Emerging studies emphasize the importance of trust in government and interpersonal relationships in reducing infections and fostering positive vaccine attitudes. This study investigates the relationship between HCWs' trust in the workplace and government and depressive symptoms during the pandemic. The COVID-19 HEalth caRe wOrkErS study surveyed 32,410 HCWs from 22 countries, including clinical and nonclinical staff. Participants completed the Patient Health Questionnaire-9 and ad-hoc questions assessing trust in the workplace and government. Logistic regression and multilevel models examined associations between trust levels and depressive symptoms. High workplace trust (OR = 0.72 [0.68, 0.76]) and government trust (OR = 0.72 [0.69, 0.76]) were linked to lower odds of depressive symptoms, with significant between-country variation. Country-level analyses showed that workplace trust was more protective in more developed countries and under stricter COVID-19 restrictions. Despite cross-country variation, HCWs with higher trust in the workplace and government had ~28% lower odds of experiencing depressive symptoms compared to those with lower trust. Promoting trust may help mitigate the mental health impact of future crises on 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".