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Record W4392285217 · doi:10.1002/cl2.1382

PROTOCOL: Effects of social prescribing for older adults: An evidence and gap map

2024· article· en· W4392285217 on OpenAlex
Elizabeth Tanjong Ghogomu, Vivian Welch, Mojde Yaqubi, Omar Dewidar, Victoria Barbeau, Srija Biswas, Kiffer G. Card, Sonia Hsiung, Caitlin Muhl, Michelle Nelson, Douglas M Salzwedel, Marianne Saragosa, Cindy Yu, Kate Mulligan, Paul C. Hébert

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCampbell Systematic Reviews · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of British ColumbiaPublic Health OntarioUniversity of TorontoQueen's UniversitySimon Fraser UniversityCentre Hospitalier de l’Université de MontréalCanadian Red Cross SocietyBruyèreUniversity of Ottawa
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsProtocol (science)PsychologyGerontologyMedicineAlternative medicine

Abstract

fetched live from OpenAlex

Objectives This is the protocol for an evidence and gap map. The objectives are as follows: The aim of this evidence and gap map is to map the available evidence on the effectiveness of social prescribing interventions addressing a non-medical, health-related social need for older adults in any setting. Specific objectives are as follows: 1.To identify existing evidence from primary studies and systematic reviews on the effects of community-based interventions that address non-medical, health-related social needs of older adults to improve their health and wellbeing.2.To identify research evidence gaps for new high-quality primary studies and systematic reviews.3.To highlight evidence of health equity considerations from included primary studies and systematic reviews.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Not applicablemedium
gptno category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.001
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.480
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.135
GPT teacher head0.375
Teacher spread0.240 · 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