The presence of psychological distress in healthcare workers across different care settings in Windsor, Ontario, during the COVID-19 pandemic: A cross-sectional study
Bibliographic record
Abstract
Introduction: Few studies have examined psychological distress in healthcare workers (HCWs) across the care continuum. This study describes distress levels reported by HCWs across care settings and factors associated with distress. Methods: A cross-sectional survey of HCWs from Windsor, Ontario, was conducted between May 30th, 2020, and June 30th, 2020. The survey included the Kessler Psychological Distress Scale (K10), sociodemographic, frontline status, perceptions of training, protection, support, respect among teams, and professional and personal stressors. Univariate analyses were used to compare across settings and multivariate logistic regression assessed factors associated with distress. Results: Four hundred and three HCWs from the hospital (49.4%), community health and social service (18.4%), first responder (14.7%), primary care (7.9%), home (6.0%), and long-term care (LTC; 4.0%) participated in the survey. Common concerns included fear of transmitting COVID-19 to family, safety on the job, and balancing personal care with work demands. LTC and home-care HCWs reported greater concern about workload and staffing levels, whereas community health workers were more anxious about their financial security. Overall, 228 (74.2%) HCWs who completed the K10 reported high distress, with greater rates among hospital and LTC HCWs. Distress was more likely in HCWs who identified as female, younger than 55, perceived lower respect among team, and experienced greater worry about physical and mental health and managing high workloads. Conclusion: Results showed a high degree of distress experienced by HCWs across care settings and the impact of the COVID-19 pandemic on personal and work-related stress. Promoting self-care and supportive and collaborative healthcare teams are promising avenues for mitigating symptoms of distress.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".