Effects of the COVID-19 Pandemic on Healthcare Providers: Policy Implications for Pandemic Recovery
Bibliographic record
Abstract
BACKGROUND: Notably higher rates of mental health issues have been reported among healthcare providers (HCPs) during the COVID-19 pandemic. Concerns over the impact of policy decisions on the well-being of HCPs is growing, yet it remains underexplored in the literature. METHOD: HCPs from a 301-bed mental health hospital and a 408-bed acute care community hospital, both located in central Ontario, participated in interviews (N = 30) and answered open-ended questionnaires (N = 88) to provide their experiences with the COVID-19 pandemic. RESULTS: Using interpretive description methods, we found that public health policies and other strategies intended to mitigate COVID-19 transmission variably impacted HCP well-being and professional practice. DISCUSSION: Pandemic-related policies contributed to HCP stress by changing the healthcare environment and clinical practice. Understanding HCP experiences is key for leaders, policy makers and health system planners to deal with the current state, recovery and preparation for future pandemics. Direct input into policy development, implementation and evaluation from HCPs may support their well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".