Modeling factors explaining physicians’ satisfaction with competence
Bibliographic record
Abstract
OBJECTIVE: Attention to physician wellness has increased as medical practice gains in complexity. Physician satisfaction with practice is critical for quality of care and practice growth. The purpose of this study was to model physicians' self-reported Satisfaction with Competence as a function of their perceptions of the Quality of Health Services, Distress, Coping, Practice Management, Personal Satisfaction and Professional Equity. METHODS: Comprehensive questionnaires were sent to a stratified sample of 5300 physicians across Canada. This cross-sectional study focused on physicians who examined and treated individual patients for a final study population of 2639 physicians. Response bias was negligible. The questionnaires contained measures of Satisfaction with Competence, Quality of Health Services, Distress, Coping, Personal Satisfaction, Practice Management and Professional Equity. Exploring relationships was done using Pearson correlations and one-way analysis of variance. Modeling was by hierarchical regressions. RESULTS: The measures were reliable: Satisfaction with Competence (α = .86), Quality (α = .86), Access (α = .82), Distress (α = .82), Coping (α = .76), Personal Satisfaction (α = .78), Practice Management (α = .89) and the dimensions of Professional Equity (Fulfillment, α = .81; Financial, α = .93; and Recognition, α = .75) with comparative validity. Satisfaction with Competence was positively correlated with Quality (r = .32), Efficiency (r = .37) and Access (r = .32); negatively correlated with Distress (r = -.54); and positively correlated with Coping strategies (r = .43), Personal Satisfaction (r = .57), Practice Management (r = .17), Fulfillment (r = .53), Financial (r = .36) and Recognition (r = .54). Physicians' perceptions on Quality, Efficiency, Access, Distress, Coping, Personal Satisfaction, Practice Management, Fulfillment, Pay and Recognition explained 60.2% of the variation in Satisfaction with Competence, controlling for years in practice, self-reported health and duties of physicians. CONCLUSION: Satisfaction with Competence could be affected by excessive accumulation of duties, concerns about quality, efficiency, access, excessive distress, inadequate coping abilities, personal satisfaction with life as a physician, challenges in managing practices and persistent inequities among physicians.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".