Systematic review of social prescribing and older adults: where to from here?
Bibliographic record
Abstract
OBJECTIVE: Social prescribing is a person-centred model of care with emphases on lessening the impact of unmet social needs, supporting the delivery of personalised care, and reducing non-medical resource use in the primary care setting. The purpose of this systematic review was to synthesise the effect of social prescribing for older adults within primary care. DESIGN: We followed standard systematic review guidelines, including protocol registration, screening studies (title/abstract and full text) and assessing the study quality. ELIGIBILITY AND INFORMATION SOURCES: We searched multiple online databases for studies that included older adults 60+ years (group mean age), an intervention defined and called social prescribing (or social prescription) via health provider referrals to non-medical services, and quantitative physical and psychosocial outcomes and/or health resource use. We included experimental and observational studies from all years and languages and conducted a narrative synthesis. The date of the last search was 24 March 2022. RESULTS: We screened 406 citations (after removing duplicates) and included seven studies. All studies except one were before-after design without a control group, and all except one study was conducted in the UK. Studies included 12-159 participants (baseline), there were more women than men, the group mean (SD) age was 76.1 (4.0) years and data collection (baseline to final) occurred on average 19.4 (14.0) weeks apart. Social prescribing referrals came from health and social providers. Studies had considerable risk of bias, programme implementation details were missing, and for studies that reported data (n=6) on average only 66% of participants completed studies (per-protocol). There were some positive effects of social prescribing on physical and psychosocial outcomes (eg, social participation, well-being). Findings varied for health resource use. These results may change with new evidence. CONCLUSIONS: There were few peer-reviewed studies available for social prescribing and older adults. Next steps for social prescribing should include co-creating initiatives with providers, older people and communities to identify meaningful outcomes, and feasible and robust methods for uptake of the prescription and community programmes. This should be considered in advance or in parallel with determining its effectiveness for meaningful outcomes at multiple levels (person, provider and programme).
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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 | 0.026 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".