Linking Social Prescribing and Lifestyle Redesign <sup>®</sup> : A Step Toward Health and Equity?
Bibliographic record
Abstract
Background. Presented as a response to unmet social needs and a way toward a more equitable, sustainable, and healthier society, social prescribing (SP) is gaining international attention. However, it also faces challenges and criticisms, particularly regarding its evidence base, implementation processes, and relevance across diverse contexts. To date, little is known about its compatibility with preventive occupational therapy, notably with Lifestyle Redesign (LR), a landmark intervention that shares similarities with SP. Purpose. To synthesize knowledge about SP, its areas of compatibility and tension, as well as its linkages with LR. Key issues. While evidence regarding SP is conflicting, a critical examination of its connection with LR could help better meet the complex and evolving needs of older adults, especially those facing structural or social marginalization. Given their affinities, SP and LR present several promising points of integration, including: (1) offering LR within social prescription, (2) assigning a dedicated SP navigator to LR, and (3) positioning occupational therapists as SP navigators. Implications. Linking social prescribing and LR could contribute to tackling global public health priorities such as loneliness, social isolation, and chronic diseases, while advancing knowledge and practices that empower occupational therapists to address social determinants of health.
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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 | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".