The impact of remuneration, extrinsic and intrinsic incentives on interprofessional primary care teams: results from a rapid scoping review
Bibliographic record
Abstract
BACKGROUND: High-performing primary care relies on effective interprofessional teams and provider payment arrangements. This study aims to examine the impact of provider remuneration mechanisms and intrinsic and extrinsic incentives in team-based primary care. METHODS: This rapid scoping review assessed various provider payment models and incentives in team-based primary care. Statistical tests were not applicable in this review. RESULTS: Fee-for-service models hindered team collaboration, while salaried and quality-based compensation models enhanced collaboration. Extrinsic incentives, such as pay-for-performance programs for physicians, showed mixed impacts on outcomes. Strong organizational cultures and leadership, resources, team meetings, training, clear protocols, and professional development opportunities facilitated teamwork. Intrinsic incentives like autonomy, mastery, and social purpose improved team performance and satisfaction. CONCLUSIONS: This study underscores the importance of a holistic approach to designing interprofessional primary care teams. It highlights the need for implementing non-fee-for-service provider payment models and team-based pay-for-performance incentives. Investments in teams should include health human resources and leadership, training, guidelines, and professional development opportunities. Implementing a performance measurement framework for teams and regular public reporting can foster mastery. Continuous research and evaluation are crucial to optimizing teamwork and healthcare delivery in primary care settings.
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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 | 0.076 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".