An evaluation of the role of social identity processes for enhancing health outcomes within <scp>UK</scp>‐based social prescribing initiatives designed to increase social connection and reduce loneliness: A systematic review
Bibliographic record
Abstract
Abstract The UK's National Health Service has introduced Social Prescribing initiatives to tackle loneliness and ill‐health, yet it lacks a theoretical foundation and evidence base for Social Prescribing's effectiveness. Recent research applies the Social Identity Approach to Health (SIAH) to explain Social Prescribing's health benefits, emphasising how social connection unlocks health‐enhancing psychological mechanisms. This systematic review therefore aims to assess UK‐based Social Prescribing programmes designed to boost social connection and alleviate loneliness, examining programme efficacy and the role of SIAH processes in health outcomes. Following PRISMA guidelines, a narrative synthesis of articles published from May 5, 2006 (when social prescribing was first introduced in the NHS), to April 8, 2024, was conducted, and their quality assessed using CONSORT‐SPI (2018). Of these programmes, 10 employed a mixed‐methods design, 8 qualitative and 1 quantitative service evaluation, totalling 3,298 participants. Results indicate that Social Prescribing's psychological value lies in quality rather than quantity of social connections, with meaningful connections fostering shared identity, perceived support and self‐efficacy, the latter of which sustains social engagement post‐programme. The SIAH was a useful tool for mapping mixed‐methods findings onto a common theoretical framework to highlight these key proponents. Overall, this review underscores the importance of SIAH‐informed Social Prescribing interventions in enhancing social connectedness, reducing loneliness, and promoting overall health. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement .
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".