Comprehensive Review of the Success of the Hospital-at-Home Program in Today's Healthcare Delivery
Bibliographic record
Abstract
Background: Social prescribing (SP) is a new approach of primary care with the aim of addressing patients' social, emotional, and practical needs (e.g., loneliness and mental health), which traditional medicine is ill-equipped to resolve. Given that 20% of general practice consultations are related to social needs, SP is timely. Aim: To review the implementation of social prescribing (SP) in general practice, including models of delivery, health and wellbeing outcomes, facilitators, barriers, and scalability. Methods: Following PRISMA 2020, we systematically searched MEDLINE, Embase, CINAHL, PsycINFO, and grey literature from 2000 to October 2024. Sixty-eight studies were synthesized narratively using the GRADE approach and thematic analysis, with systematic reviews, primary research, and policy reports included. Results: SP models differ across the globe: the UK is the only country in which the link worker model is formalized, while Canada and Australia have less established approaches. Improved mental health, social connectivity, and reduced food insecurity are associated with SP; evidence for physical health and healthcare use is mixed. Important facilitators include funding and training, while barriers include competing demands on resources and gaps in the evidence base. Conclusions: SP adds strength to general practice by addressing social determinants of health. Standardised evaluation and developing strategies for scalability and equitable access to programs will be important in maximising the impact of SP efforts.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.014 |
| 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".