Payment method as a predictor of daily distress experienced by physicians
Bibliographic record
Abstract
Background: Physicians face intrinsic tensions when practicing medicine; therefore, extrinsic factors that could affect distress, such as payment methods, need to be assessed. The study objectives were to: compare levels of distress by payment method, identify factors predicting distress in a two-level regression model, and explore interactions between predictors of distress and payment method. Methods: A cross-sectional study was conducted among physicians in the Saskatoon Health Region, Saskatchewan. Physicians completed a pre-tested questionnaire about their distress. Analysis of variance was used to compare distress levels of physicians paid by fee-for-service (FFS), alternative payment plans (APPs), and blended methods. A mixed linear regression model was built to predict distress with geographical area of practice as the random component. Demographics, workload, complexity of patients, payment method, career satisfaction, and practice profile were the independent variables. The interactions between payment method and predictors of daily distress were evaluated. Results: A total of 382 physicians participated (response rate = 48.1%). Response bias was tested and found to be negligible. In the multivariable analysis, payment method was a predictor of distress which interacted with the proportion of complex cases. Lower levels of distress were found among physicians who had more than 75% of patients with complex conditions and were paid by APPs, compared to those paid by FFS and blended methods. Career satisfaction was found to be an important predictor. Nine percent of the outcome variation was explained by geographic area of practice. Conclusions: Payment method is a predictor of distress when adjusting by confounders, interacting with proportion of complex cases. APPs may promote provision of care for patients with complex conditions. Career satisfaction can be considered a protective indicator of distress. Practice environment influences distress experienced by 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".