The Incorporation of Social Prescribing in General Practice: A Review of Models, Outcomes, and Scalability
Bibliographic record
Abstract
Background: Social prescribing (SP) is a developing intervention that links patients in primary care with local non-medical services to address the social, emotional, and practical needs of patients, such as loneliness and mental health, that cannot be addressed by traditional medicine. Approximately 20% of consultations in general practice are related to social issues, making SP very relevant to practice. Aim: This literature review evaluates the implementation of SP in general practice in terms of the implementation process, health and wellbeing outcomes, facilitators, barriers, and optimization strategies. Methods: A systematic search of MEDLINE, Embase, CINAHL, PsycINFO, and grey literature from 2000 to October 2024 was conducted in accordance with improvements to the practice of SP in GP and other primary care contexts PRISMA 2020 guidelines, identifying 68 studies (10 systematic reviews, 33 primary studies, and 25 grey literature reports). Findings were synthesized narratively following the GRADE approach, and key themes were ascertained through thematic analysis. Results: SP delivery varies globally. The UK model is formalized around link workers, while Canada and Australia offer considerably fewer formal connections to SP. Outcomes include improved mental health, social connectedness, and decreased food insecurity, but physical health and healthcare use outcomes were more varied. Facilitators include funding and training; barriers include gaps in the evidence base and limitations of resources. Conclusions: SP consolidates general practice with its emphasis on social determinants, but rigorous evaluations and standardization are required. Scalability and equitable access solutions are the keys to impact.
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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 | 0.101 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.021 | 0.027 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".