Factors that influence specialist physician preferences for fee-for-service and salary-based payment models: A qualitative study
Bibliographic record
Abstract
Most physicians across the world are paid through fee-for-service. However, there is increased interest in alternative payment models such as salary, capitation, episode-based payment, pay-for-performance, and strategic blends of these models. Such models may be more aligned with broad health policy goals such as fiscal sustainability, delivery of high-quality care, and physician and patient well-being. Despite this, there is limited research on physicians' preferences for different models and a disproportionate focus on differences in income over other issues such as physician autonomy and purpose. Using qualitative interviews with 32 specialist physicians in Alberta, Canada, we examined factors that influence preferences for fee-for-service (FFS) and salary-based payment models. Our findings suggest that a series of factors relating to (1) physician characteristics, (2) payment model characteristics, and (3) professional interests influence preferences. Within these themes, flexibility, autonomy, and compatibility with academic roles were highlighted. To encourage physicians to select a specific payment model, the model must appeal to them in terms of income potential as well as non-monetary values. These findings can support constructive discussions about the merits of different payment models and can assist policy makers in considering the impact of payment reform.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".