Does shifting a physician payment system shift physician priorities? A multi-site evaluation of an alternative payment plan (APP) for gynecologic oncologists in Ontario.
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to attempt to understand how changing the mode of reimbursement alters physician behavior from the physician's perspective. METHOD: Individual interviews were conducted with 14 Ontario gynecologic oncologists. Each interview was analyzed using grounded theory. RESULTS: The move to an alternative payment plan (APP) significantly shifted physician clinical and personal priorities. This resulted in improvements in recruitment and retention. A model was developed to explain the link between the shift in the payment system and physician perceptions of their behavior. The model is comprised of two themes: (a) need for change: site similarities and differences, (b) effects of change: shifting priorities and time management. Even when the same compensation package was offered to four sites, the interpretations and motivations differed from site to site. We identified two types of situations: sites that were operating in 'survival mode' and those that were 'meeting core clinical and academic requirements'. They experienced the APP very differently. CONCLUSION: This study presents a model that depicts how and why a funding shift has variable effects on physician behaviors, depending on the individual physician, site, and multi-site perspectives. It offers one of the few qualitative evaluations of a funding change.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".