From Policy to Practice: Barriers in Social Prescribing between the UK and Canada
Bibliographic record
Abstract
Social prescribing is an emerging model of care that connects individuals to non-clinical supports to address social determinants of health. While the concept originated and is more widely integrated in the United Kingdom, interest in social prescribing is growing in Canada. As both countries continue to explore and expand social prescribing efforts, there is a need to synthesize evidence on how initiatives are structured, implemented, and evaluated across these two contexts. This systematic review aims to identify, compare, and analyze existing literature on social prescribing initiatives in Canada and the United Kingdom. The review will highlight key similarities, differences, strengths, and gaps in each country’s approach to social prescribing. It will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search will be conducted with a 25-year date limit but no restrictions on language. Databases to be searched include PubMed, Web of Science, Ovid Medline and Scopus, alongside grey literature sources such as Google Scholar, relevant government and organizational websites, and reports from social prescribing networks in both countries. Additionally, we contacted the Canadian Institute for Social Prescribing (CISP) to identify supplementary resources and relevant gray literature. The primary reviewer will conduct screening, full-text assessment, and data extraction, with verification and support provided by a second reviewer (the project supervisor). Findings will be analyzed both thematically and descriptively, and results will be presented in both tabular and narrative form to inform future policy, practice, and research directions.
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.
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 | 0.071 | 0.239 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".