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Record W4399713244 · doi:10.21606/drs.2024.707

Design for social prescribing: bridging silos for health promotion

2024· article· en· W4399713244 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of DRS · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsBridging (networking)Information siloWorkplace health promotionComputer scienceBusinessHealth promotionComputer securityPublic healthEngineeringMedicineNursingSiloMechanical engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.158
GPT teacher head0.343
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it