How Can We Help Healthcare Workers during a Catastrophic Event Such as the COVID-19 Pandemic?
Bibliographic record
Abstract
Healthcare workers (HCWs) have significantly suffered during the COVID-19 pandemic, reporting a high prevalence of anxiety, depression and post-traumatic stress disorder (PTSD). We investigated with this survey whether HCWs benefitted from supportive measures put in place by hospitals and how these measures were perceived. This cross-sectional survey, which was conducted during the first wave of COVID-19 at the Geneva University Hospitals, Switzerland, between May and July 2021, collected information on the use and perception of practical and mental health support measures provided by the hospital. In total, 3461 HCWs participated in the study. Regarding the practical support measures, 2896 (84%) participants found them useful, and 2650 (76%) used them. Regarding the mental health support measures, 3149 (90%) participants found useful to have the possibility of attending hypnosis sessions, 3163 (91%) to have a psychologist within hospital units, 3202 (93%) to have a medical nursing psychiatric permanence available seven days a week, and 3171 (92%) to have a hotline available seven days a week. In total, 436 (13%) HCWs used at least one of the available mental health support measures. During the COVID-19 pandemic, the support measures were valued by HCWs. Given the high prevalence of psychiatric issues among HCWs, these measures seem necessary and are likely to have alleviated the suffering 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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".