Family Physicians’ Perceived Needs Regarding Their Mental Health and Wellbeing in Infectious Catastrophic Events: A Mixed Studies Literature Review
Bibliographic record
Abstract
INTRODUCTION: During catastrophes, physicians face significant stress and emotional challenges. This review explored existing evidence about the challenges family physicians face during infectious catastrophes, and their perceived well-being needs. MATERIALS AND METHODS: We conducted a mixed studies literature review using 2 databases, Ovid MEDLINE ALL (1946 to February 2023) and PsycInfo on Ovid (1806 to February 2023). To assess methodological quality, we used Mixed Methods Appraisal Tool. The extracted data were analyzed employing a data-based convergent mixed methods design. RESULTS: Thirty-four (34) studies met the criteria for data extraction. Line-by-line coding for thematic analysis was applied to Result and Discussion sections of included articles. Findings were categorized into 4 levels: Societal, Institutional, Organizational, and Individual. Seven themes were identified in total. DISCUSSION: Public health authorities should focus on systemic changes, including organizational development to improve coordination within and across organizations. Clinician involvement in decision-making, clear communication, mental health support, and adequate resources are crucial. Policy implications underscore the necessity for healthcare policies prioritizing physician well-being, and organizational support during infectious catastrophes. Improving work conditions extends beyond personal protective equipment (PPE) access, requiring swift betterment of service innovations, with ongoing reassessment for sustainable care planning, financing, and delivery beyond emergencies.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".