Linking Social Prescribing and Preventive Occupational Therapy: A Way to Advance Health and Equity?
Bibliographic record
Abstract
Abstract Presented as a response to unmet social needs and a way to foster a more equitable, sustainable, and healthy society, social prescribing (SP) faces challenges and criticisms, and little is known about its compatibility with preventive occupational therapy, particularly the Lifestyle Redesign program (LR), a landmark intervention with shared principles. To explore this compatibility this narrative umbrella review draws on seven systematic reviews supplemented by expert contributions from the National Academy of Social Prescribing. Results indicate conflicting evidence of SP on individuals, communities and healthcare systems. Despite widespread implemented in the UK and gaining traction globally, including in Canada and U.S., few peer-reviewed studies have examined its effects on older adults, and those available are of limited quality. Most studies reported positive changes in health self-management, loneliness, social isolation, and well-being, with high acceptability among participants and general practitioners. The COVID-19 pandemic highlighted both the importance of social prescribing in addressing these issues and the challenges of its implementation during crises. SP remains underutilized by occupational therapists, even though their expertise aligns with its objectives. Strengthening SP-LR linkages could advance interdisciplinary collaboration through three approaches: (1) LR as an activity within SP, (2) a navigator dedicated to LR, and (3) the occupational therapist as navigator. While not a unique solution, integrating SP within occupational therapy practice could improve access to preventive interventions, notably for older adults at risk of loneliness, social isolation, and chronic disease, and empower clinicians to address social determinants of health and drive innovation in gerontology.
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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 | 0.018 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".