Supporting social prescribing in primary care by linking people to local assets: a realist review
Bibliographic record
Abstract
BACKGROUND: Social prescribing is a way of addressing the 'non-medical' needs (e.g. loneliness, debt, housing problems) that can affect people's health and well-being. Connector schemes (e.g. delivered by care navigators or link workers) have become a key component to social prescribing's delivery. Those in this role support patients by either (a) signposting them to relevant local assets (e.g. groups, organisations, charities, activities, events) or (b) taking time to assist them in identifying and prioritising their 'non-medical' needs and connecting them to relevant local assets. To understand how such connector schemes work, for whom, why and in what circumstances, we conducted a realist review. METHOD: A search of electronic databases was supplemented with Google alerts and reference checking to locate grey literature. In addition, we sent a Freedom of Information request to all Clinical Commissioning Groups in England to identify any further evaluations of social prescribing connector schemes. Included studies were from the UK and focused on connector schemes for adult patients (18+ years) related to primary care. RESULTS: Our searches resulted in 118 included documents, from which data were extracted to produce context-mechanism-outcome configurations (CMOCs). These CMOCs underpinned our emerging programme theory that centred on the essential role of 'buy-in' and connections. This was refined further by turning to existing theories on (a) social capital and (b) patient activation. CONCLUSION: Our realist review highlights how connector roles, especially link workers, represent a vehicle for accruing social capital (e.g. trust, sense of belonging, practical support). We propose that this then gives patients the confidence, motivation, connections, knowledge and skills to manage their own well-being, thereby reducing their reliance on GPs. We also emphasise within the programme theory situations that could result in unintended consequences (e.g. increased demand on GPs).
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".