A contingency theory model of primary physicians compensation mix
Bibliographic record
Abstract
Background There is strong evidence that primary care teams providing services with high accessibility, continuity, and comprehensiveness have a key role to play in improving the health and well-being of populations. The compensation model used to pay primary care physicians is one of the actionable factors affecting clinical practice and primary care delivery performance. We used data from Quebec (Canada) to build a conceptual model aimed at optimizing the correspondence between the compensation model mix and other parameters that influence clinical practice. Methods This study is based on the integrated analysis of three data sources. First, we conducted a longitudinal analysis (2006-2015) of available primary care physicians' compensation models and billing rules in Quebec (Canada). Second, we analyzed quantitative data on physicians' compensation expenditures, as well as characteristics of physicians, services, and patients. Finally, we conducted in-depth qualitative interviews with physicians, experts, and physician billing firms (n = 16). The study was conducted in Quebec, where 97% of primary care physicians work within a single-payer public system. Results Our results tally with those of other studies in the field suggesting the method used to pay primary care physicians is neither the only nor the most influential factor structuring clinical practice. The conceptual model that we designed belongs to ‘contingency theory’ approaches, according to which the desirability of a given compensation model is contingent upon the correspondence between influences of the larger practice environment and expected outcomes. Conclusions Optimizing primary care physicians' compensation models considering both other determinants of clinical practice and expected outcomes should be part of policy agendas aimed at strengthening primary care delivery. Key messages: This conceptual model show that the desirability of a given compensation model is contingent upon the correspondence between influences of the larger practice environment and expected outcomes Optimizing primary care physicians' compensation models considering both other determinants of clinical practice and expected outcomes could strengthen primary care delivery
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".