A comparison of social prescribing approaches across twelve high-income countries
Bibliographic record
Abstract
BACKGROUND: Social prescribing connects patients with community resources to improve their health and well-being. It is gaining momentum globally due to its potential for addressing non-medical causes of illness while building on existing resources and enhancing overall health at a relatively low cost. The COVID-19 pandemic further underscored the need for policy interventions to address health-related social issues such as loneliness and isolation. AIM: This paper presents evidence of the conceptualisation and implementation of social prescribing schemes in twelve countries: Australia, Austria, Canada, England, Finland, Germany, Portugal, the Slovak Republic, Slovenia, the Netherlands, the United States and Wales. METHODS: Twelve countries were identified through the Health Systems and Policy Monitor (HSPM) network and the EuroHealthNet Partnership. Information was collected through a twelve open-ended question survey based on a conceptual model inspired by the WHO's Health System Framework. RESULTS: We found that social prescribing can take different forms, and the scale of implementation also varies significantly. Robust evidence on impact is scarce and highly context-specific, with some indications of cost-effectiveness and positive impact on well-being. CONCLUSIONS: This paper provides insights into social prescribing in various contexts and may guide countries interested in holistically tackling health-related social factors and strengthening community-based care. Policies can support a more seamless integration of social prescribing into existing care, improve collaboration among sectors and training programs for health and social care professionals.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".