Dublin hospital workers’ mental health during the peak of Ireland’s COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Hospital-based healthcare workers have experienced significant psychological stressors during the COVID-19 pandemic. AIM: To evaluate the mental health of hospital workers during the third wave of the COVID-19 pandemic in Dublin, Ireland. METHODS: Cross-sectional anonymous online survey of hospital workers (n = 377; 181 doctors (48.0%), 166 nurses (44.0%), 30 radiographers (8.0%)), collecting demographic information, COVID-19 exposure history and mental health measures. RESULTS: There were significant differences between profession groups in gender, experience, COVID-19 infection history, exposure to COVID-19 positive acquaintances, and work areas. Moderate-severe post-traumatic stress disorder (PTSD) symptoms were found in 45.1% (95% CI 40.1-50.1%) of all participants; significantly fewer doctors reported moderate-severe PTSD symptoms (26%; 95% CI 22-36%). A World Health Organisation-5 Wellbeing Index (WHO-5) score ≤ 32, indicating low mood, was reported by 52% (95% CI 47-57%) of participants; significantly fewer doctors reported low mood (46%; 95% CI 39-53%). One-week suicidal ideation and planning were reported respectively by 13% (95% CI 10-16%) and 5% (95% CI 3-7%) of participants with no between-group differences. Doctors reported significantly less moral injury than other groups. There were no significant between-group differences regarding coping styles. Work ability was insufficient in 39% (95% CI 34-44%) of staff; no between-group differences. CONCLUSIONS: Dublin hospital workers reported high levels of PTSD symptoms, mood disturbance, and moral injury during the COVID-19 pandemic. Concerning levels of suicidal ideation and planning existed in this cohort. Differences in degrees of post-traumatic stress, moral injury, and wellbeing were found between profession groups, which should be considered when planning any supports.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".