Impact of payment methods on professional equity of physicians
Bibliographic record
Abstract
Background: Professional equity, evaluation of own contributions and received rewards compared to contributions and rewards of other physicians has not been assessed according to payment methods. The aim of this study is to compare levels of professional equity among physicians paid by fee-for-service (FFS), alternative payment plans (APPs), and blended schemes. Methods: In 2011, medical practitioners in the Saskatoon Health Region, Saskatchewan, were surveyed using a questionnaire developed for physicians to measure professional equity. Intangible rewards were measured by the dimensions of fulfilment and recognition, and tangible rewards by the dimension of income. The three-dimensional structure of the questionnaire was first corroborated through a confirmatory factor analysis (CFA). Analyses of variances were then performed to account for differences in the levels of professional equity. A linear regression model predicting levels of professional equity was used to test the interaction between specialty and payment method, controlling by number of patients, gender, and age group. Results: In total, 382 (48.1%) physicians participated: 35.6% were family/general practitioners (FPs); 61% were clinical/surgical specialists; and, 3.4% were pathologists. The internal structure of the questionnaire was confirmed by the CFA. Physicians paid by FFS perceived lower professional equity than those paid by APP (p = .005). Practitioners under APPs reported higher levels of income (p = .03) and recognition (p = .001) equity than those with FFS. FPs perceived lower fulfilment (p = .003) and income (p = .008) equity compared to medical-surgical specialists. Furthermore, controlling by number of patients seen per week, higher levels of professional equity are predicted among FPs paid by APPs and blended schemes in comparison to FPs paid by FFS. Conclusions: Higher levels of professional equity were perceived among physicians paid by APPs in comparison to those paid by FFS. Physicians paid by APPs considered that they are receiving fair economic rewards and appropriate recognition. In addition, enhanced levels of professional equity could be predicted among FPs with APPs and blended schemes. APPs could be explored to improve the professional equity of FPs and, indirectly, promote improved primary health care.
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 | 0.020 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".