A comparison of social prescribing approaches across twelve high-income countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it