Bibliographic record
Abstract
Social prescribing (SP) refers patients to community and social services that sup-port the individual’s social needs and that can bolster their overall health and well-being. SP offers a promising approach to addressing wide-spread mental health issues, social determinants of health, and growing social isolation. While SP is integrated into the national health systems of countries such as the United Kingdom, Canada, Australia, and Japan, it has only recently begun to take root in the United States (US). This paper presents “Design for Social Prescribing”, a re-search project led by the Design Laboratory at the Harvard T.H. Chan School of Public Health that explored how the structured use of design could help expand and accelerate the SP in the US. The research was structured on advanced design models to support multi-stakeholder collaboration in three phases. This paper outlines key learnings from these phases, including their processes, approaches, and outcomes.
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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 | 0.062 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".