Do compensation models affect family physician job satisfaction?
Bibliographic record
Abstract
OBJECTIVE: To explore how factors associated with various compensation models affect job satisfaction of family physicians. DATA SOURCES: . STUDY SELECTION: To be included articles had to be peer reviewed, at least 50% of study participants had to be family physicians practising longitudinal or comprehensive care, and articles had to address career satisfaction in relation to compensation models. Twenty-seven studies were included. SYNTHESIS: An extraction form was used to synthesize key details from each study, followed by thematic analysis. Four predominant job satisfaction factors were identified: workload or administrative burden, autonomy, income security, and justice or fairness of compensation. Five distinct models, representing both direct and indirect compensation, were identified in the literature most frequently: salaried, fee-for-service, capitation, loan repayment programs or incentives, and pay-for-performance. Each payment model had merits and drawbacks in relation to job satisfaction. Salaried physicians tended to experience less stress associated with administrative and management responsibilities; capitation models appeared to be associated with less workload stress; and fee-for-service models tended to be associated with a greater sense of autonomy. Income security, as provided by capitation and salaried models, was generally positively associated with job satisfaction. CONCLUSION: Use of blended models has the potential to address job satisfaction issues uncovered in this review and to maximize satisfaction among family physicians. Current changes and enhancements being made to compensation models in Canada present opportunities to further study their effects on family physician career satisfaction and attractiveness of the profession.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".