Exploring Ambiguity in Social Prescribing: Creating a Typology of Models Based on a Scoping Review of Core Components and Conceptual Elements in Existing Programs
Bibliographic record
Abstract
Introduction: Despite a growing social prescribing (SP) movement, the concept itself remains ambiguous, resulting in considerable variation in practice across different contexts. As discussions of SP emerge at the policy level, this ambiguity may influence program and policy spread. This research explores current literature and practices of SP to understand the different conceptual elements (i.e., goals and motivations) and core components (i.e., structures and behaviors) of SP. Methods: A scoping review of academic literature was conducted. Search terms included “social prescr ∗ ,” “social medicine,” “link worker,” “social refer ∗ ,” and “community refer ∗ .” From this data, a typology of models based on descriptions of conceptual elements and core components was developed. Following the development of the typology, we applied our findings to existing SP programs, which were found through reference‐checking academic sources and a community consultation conducted by our partner organization. Results: 29 articles informed the scoping review, and 24 program descriptions were compared to the typology. Four models of SP are proposed. The clinical model follows a biomedical approach where the physician refers patients to SP services primarily geared to lifestyle factors. The holistic model addresses social determinants of health, referring to food security, housing, or income programs. The empowerment model considers well‐being through relationship‐building and a deep understanding of the client context. The healthy community’s model focuses on community development, where community members gain the capacity to better support one another. Conclusion: The proposed typology will help implementers design programs that best suit their goals and contexts. At the policy level, this research demonstrates how a “bounded ambiguity” approach may allow SP to demonstrate its fullest potential. For researchers, this work provides a foundation for future exploration of how SP is implemented despite its ambiguity.
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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 | 0.145 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.043 | 0.039 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| 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".