Effects of the COVID-19 Pandemic on the Mental Health of Healthcare Providers: A Comparison of a Psychiatric Hospital and a General Hospital
Bibliographic record
Abstract
Background: Before the COVID-19 pandemic, healthcare providers (HCPs) were already experiencing a higher prevalence of mental health disorders compared with non-healthcare professionals. Here, we report on the psychosocial functioning and stress resilience of HCPs who worked during the COVID-19 pandemic in a large-sized psychiatric facility and a large acute care hospital, both located in central Ontario, Canada. Methods: Participants completed five validated psychometric instruments assessing depression, anxiety, and stress (The Depression, Anxiety, and Stress Scale-21, DASS-21); work-related quality of life (Work-Related Quality of Life Scale, WRQoL); resilience (Connor-Davidson Resilience Scale, CD-RISC); anxiety about the novel coronavirus (Coronavirus Anxiety Scale, CAS); and loneliness (UCLA Loneliness Scale, ULS). Participants from the psychiatric hospital (n = 94) were sampled during the easing of restrictions after the first wave in Ontario, and participants from the acute care hospital (n = 146) were sampled during the height of the second wave in Ontario. Results: Data showed that HCPs from the acute care hospital and psychiatric hospital reported similar scores on the psychometric scales. There were also no significant differences in psychometric scale scores between medical disciplines at the acute care hospital. Among all HCPs, being a nurse predicted better quality of life (p = 0.01) and greater stress resilience (p = 0.031). Conclusion: These results suggest that HCPs' psychological symptoms are similar across the hospital settings sampled. Compared to other HCPs, nurses may show a unique resiliency to the pandemic. We suggest that emergencies such as the COVID-19 pandemic have a pervasive effect on HCPs. It is important to address HCPs' mental health needs in terms of crisis management and improve resilience among all HCPs during the inter-crisis period before a new challenge arrives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".