The social cure of social prescribing: a mixed-methods study on the benefits of social connectedness on quality and effectiveness of care provision
Bibliographic record
Abstract
OBJECTIVES: This study aimed to assess the degree to which the 'social cure' model of psychosocial health captures the understandings and experiences of healthcare staff and patients in a social prescribing (SP) pathway and the degree to which these psychosocial processes predict the effect of the pathway on healthcare usage. DESIGN: Mixed-methods: Study 1: semistructured interviews; study 2: longitudinal survey. SETTING: An English SP pathway delivered between 2017 and 2019. PARTICIPANTS: Study 1: general practitioners (GPs) (n=7), healthcare providers (n=9) and service users (n=19). Study 2: 630 patients engaging with SP pathway at a 4-month follow-up after initial referral assessment. INTERVENTION: Chronically ill patients experiencing loneliness referred onto SP pathway and meeting with a health coach and/or link worker, with possible further referral to existing or newly created relevant third-sector groups. MAIN OUTCOME MEASURE: Study 1: health providers and users' qualitative perspectives on the experience of the pathway and social determinants of health. Study 2: patients' primary care usage. RESULTS: Healthcare providers recognised the importance of social factors in determining patient well-being, and reason for presentation at primary care. They viewed SP as a potentially effective solution to such problems. Patients valued the different social relationships they created through the SP pathway, including those with link workers, groups and community. Group memberships quantitatively predicted primary care usage, and this was mediated by increases in community belonging and reduced loneliness. CONCLUSIONS: Methodological triangulation offers robust conclusions that 'social cure' processes explain the efficacy of SP, which can reduce primary care usage through increasing social connectedness (group membership and community belonging) and reducing loneliness. Recommendations for integrating social cure processes into SP initiatives are discussed.
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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 | 0.040 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".