The impact of remuneration, extrinsic and intrinsic incentives on interprofessional primary care teams: results from a rapid scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it