Coproduction in Social Prescribing Initiatives: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Social prescribing (SP) takes a holistic approach to health by linking clients from clinical settings to community programs to address their nonmedical needs. The emerging evidence base for SP demonstrates variability in the design and implementation of different SP initiatives. To effectively address these needs, coproduction among clients, communities, stakeholders, and policy makers is important for tailoring SP initiatives for optimal uptake. OBJECTIVE: This study aims to explore the role of coproduction in SP initiatives. The research question is as follows: How and for what purpose has coproduction been incorporated across a range of SP initiatives for different clients? METHODS: A review of international literature will be conducted following the JBI guidelines for scoping reviews. We will search multiple databases including Scopus, MEDLINE, and the PAIS Index, as well as gray literature, from 2000 to 2023. The primary studies included will describe a nonmedical need for clients, a nonmedical SP program or initiative, coproduction of the SP program, and any follow-up. Review articles and commentaries will be excluded. Titles, abstracts, and full-text articles will be screened, and data will be extracted by at least 2 research team members using Covidence and a pilot-tested extraction template. Clients with lived experience will also participate in the research process. Findings will be descriptively summarized and thematically synthesized to answer the research question. RESULTS: The project was funded in 2023, and the results are expected to be submitted for publication in early 2025. CONCLUSIONS: Descriptions of what coproduction is meant to accomplish may differ from theoretical aspirations. Continued understanding of how coproduction has been designed and executed across varied international SP models is important for framing engagement in practice for future SP arrangements and their evaluation. We anticipate this review will guide clients, communities, stakeholders, and policy makers in further developing SP practice within health care systems. TRIAL REGISTRATION: Open Science Framework Registries B8U4Z; https://osf.io/b8u4z. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57062.
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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 | 0.136 | 0.114 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.090 | 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".