Global developments in social prescribing
Bibliographic record
Abstract
Social prescribing is an approach that aims to improve health and well-being. It connects individuals to non-clinical services and supports that address social needs, such as those related to loneliness, housing instability and mental health. At the person level, social prescribing can give individuals the knowledge, skills, motivation and confidence to manage their own health and well-being. At the society level, it can facilitate greater collaboration across health, social, and community sectors to promote integrated care and move beyond the traditional biomedical model of health. While the term social prescribing was first popularised in the UK, this practice has become more prevalent and widely publicised internationally over the last decade. This paper aims to illuminate the ways social prescribing has been conceptualised and implemented across 17 countries in Europe, Asia, Australia and North America. We draw from the 'Beyond the Building Blocks' framework to describe the essential inputs for adopting social prescribing into policy and practice, related to service delivery; social determinants and household production of health; workforce; leadership and governance; financing, community organisations and societal partnerships; health technology; and information, learning and accountability. Cross-cutting lessons can inform country and regional efforts to tailor social prescribing models to best support local needs.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".