Building physician wellness into the culture: evaluating a family physician well-being programme using the physician wellness inventory
Bibliographic record
Abstract
PURPOSE: Family physicians have a higher incidence of burnout, dissatisfaction, and disengagement compared to other medical specialties. Addressing burnout on the individual and systemic level is important to promoting wellness and preventing deleterious effects on physicians and patients. We used the Physician Wellness Inventory (PWI) to assess the effects of a wellness programme designed to equip family physicians with skills to address burnout. METHODS: The PWI is a fourteen-item 5-point Likert scale broken down into 3 scores; (i) career purpose, (ii) cognitive flexibility, and (iii) distress. The PWI was distributed to a cohort of n = 111 family physician scholars at 3 time points: January 2021, May-June 2021, and October 2021. The response rate was 96.4% at baseline, and 72.1% overall. Demographic information was collected to assess differences. The survey was distributed online through Qualtrics (Provo, UT). RESULTS: Cognitive Flexibility scores at the endpoint were higher for POC scholars than white scholars (P = 0.024). Distress scores for all groups decreased over time. Female scholars were more nervous, and anxious at the start than male scholars (P = 0.012), which decreased over time (P = 0.022). New career scholars were more likely than later career scholars to be distressed (P = 0.007), but both groups' distress decreased over time (P = 0.003). Later career scholars' feelings of being bothered by little interest or pleasure in doing things decreased more than new career scholars (endpoint: P = 0.022; overall: P = 0.023). CONCLUSIONS: The wellness programme shows improvement in PWI scores, indicating the programme content should be evaluated further for system level improvements.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".