Evaluations of Healthcare Providers’ Perceived Support From Personal, Hospital, and System Resources: Implications for Well-Being and Management in Healthcare in Montreal, Quebec, During COVID-19
Bibliographic record
Abstract
Increased stressful experiences are pervasive among healthcare providers (HCPs) during the COVID-19 pandemic. Identifying resources that help mitigate stress is critical to maintaining HCPs’ well-being. However, to our knowledge, no instrument has systematically examined how different levels of resources help HCPs cope with stress during COVID-19. This cross-sectional study involved 119 HCPs (64 nurses and 55 physicians) and evaluated the perceived availability, utilization, and helpfulness of a list of personal, hospital, and healthcare system resources. Participants also reported on their level of burnout, psychological distress, and intentions to quit. Results revealed that HCPs perceived the most useful personal resource to be family support; the most useful hospital resources were a safe environment, personal protective equipment, and support from colleagues; the most useful system resources were job protection, and clear communication and information about COVID. Moreover, HCPs who perceived having more available hospital resources also reported lower levels of psychological distress symptoms, burnout, and intentions to quit. Finally, although training and counseling services were perceived as useful to reduce stress, training was not perceived as widely available, and counseling services, though reported as being available, were underutilized. This instrument helps identify resources that support HCPs, providing implications for healthcare management.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".