Family physician satisfaction with two different academic compensation schemes.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A growing body of evidence suggests that comprehensive relative-value-based incentive plans (CRVPs) are more effective at tracking and improving academic productivity than other types of academic compensation schemes (ACSs). However, there is little literature to date exploring physician satisfaction with CRVPs. METHODS: Physicians in two academic family medicine departments in Toronto, Ontario, completed an anonymous satisfaction survey. One of these departments used a CRVP to compensate for non-clinical activities; the control group used a monthly stipend based on full-time equivalents (FTEs). RESULTS: When compared with controls, physicians compensated by a CRVP were more likely to increase their involvement in non-clinical activities, to report being "very satisfied" with their ACS, to feel that their ACS made them "more likely" to continue working in their department, and to feel that their ACS was "fair." CONCLUSIONS: Physicians in a family medicine department that used a CRVP felt a greater sense of sense of satisfaction and fairness in terms of their compensation for non-clinical activities. CRVP physicians also perceived an increased involvement in academic activities, were more likely to continue to work in their current department, and to feel that the compensation for non-clinical activities was adequate.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".