Personal, professional, and psychological impact of the COVID-19 pandemic on hospital workers: A cross-sectional survey
Bibliographic record
Abstract
OBJECTIVES: We aimed to evaluate the personal, professional, and psychological impact of the COVID-19 pandemic on hospital workers and their perceptions about mitigating strategies. DESIGN: Cross-sectional web-based survey consisting of (1) a survey of the personal and professional impact of the COVID-19 pandemic and potential mitigation strategies, and (2) two validated psychological instruments (Kessler Psychological Distress Scale [K10] and Impact of Events Scale Revised [IES-R]). Regression analyses were conducted to identify the predictors of workplace stress, psychological distress, and post-traumatic stress. SETTING AND PARTICIPANTS: Hospital workers employed at 4 teaching and 8 non-teaching hospitals in Ontario, Canada during the COVID-19 pandemic. RESULTS: Among 1875 respondents (84% female, 49% frontline workers), 72% feared falling ill, 64% felt their job placed them at great risk of COVID-19 exposure, and 48% felt little control over the risk of infection. Respondents perceived that others avoided them (61%), reported increased workplace stress (80%), workload (66%) and responsibilities (59%), and 44% considered leaving their job. The psychological questionnaires revealed that 25% had at least some psychological distress on the K10, 50% had IES-R scores suggesting clinical concern for post-traumatic stress, and 38% fulfilled criteria for at least one psychological diagnosis. Female gender and feeling at increased risk due to PPE predicted all adverse psychological outcomes. Respondents favoured clear hospital communication (59%), knowing their voice is heard (55%), expressions of appreciation from leadership (55%), having COVID-19 protocols (52%), and food and beverages provided by the hospital (50%). CONCLUSIONS: Hospital work during the COVID-19 pandemic has had important personal, professional, and psychological impacts. Respondents identified opportunities to better address information, training, and support needs.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".