Impact of remuneration, extrinsic and intrinsic incentives on interprofessional primary care teams: protocol for a rapid scoping review
Bibliographic record
Abstract
INTRODUCTION: Interprofessional teams and funding and payment provider arrangements are key attributes of high-performing primary care. Several Canadian jurisdictions have introduced team-based models with different payment models. Despite these investments, the evidence of impact is mixed. This has raised questions about whether team-based primary care models are being implemented to facilitate team collaboration and effectiveness. Thus, we present a protocol for a rapid scoping review to systematically map, synthesise and summarise the existing literature on the impact of provider remuneration mechanisms and extrinsic and intrinsic incentives in team-based primary care. This review will answer three research questions: (1) What is the impact of provider remuneration models on team, patient, provider and system outcomes in primary care?; (2) What extrinsic and intrinsic incentives have been used in interprofessional primary care teams?; and (3) What is the impact of extrinsic and intrinsic team-based incentives on team, patient, provider and system outcomes? METHODS AND ANALYSIS: We will conduct a rapid scoping review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews guidelines. We will search electronic databases (Medline, Embase, CINAHL, PsycINFO, EconLit) and grey literature sources (Google Scholar, Google). This review will consider all empirical studies and full-text English-language articles published between 2000 and 2022. Reviewers will independently perform the literature search, data extraction and synthesis of included studies. The Mixed Methods Appraisal Tool will be used to appraise the quality of evidence. The literature will be synthesised, summarised and mapped to themes that answer the research question of this review. ETHICS AND DISSEMINATION: Ethics approval is not required. Findings from this study will be written for publication in an open-access peer-review journal and presented at national and international conferences. Knowledge users are part of the research team and will assist with disseminating findings to the public, clinicians, funders and professional associations.
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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 | 0.156 | 0.186 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.019 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.077 | 0.018 |
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".