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Record W4415622473 · doi:10.1177/10497315251389547

Advancing Social Care Through Evidence: A Position Statement from the International Network for Social Intervention Assessment (INSIA)

2025· article· en· W4415622473 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch on Social Work Practice · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsInstitut National d'Excellence en Santé et en Services Sociaux
Fundersnot available
KeywordsIntervention (counseling)General partnershipSocial workSocial network (sociolinguistics)Social careSocial positionPosition (finance)Field (mathematics)

Abstract

fetched live from OpenAlex

The International Network for Social Intervention Assessment (INSIA) is a partnership of organizations that produce, support, and use evidence synthesis in social care. It emerges as a pivotal entity that supports evidence-informed social care decision-making. The paper argues for the importance of the production and use of trustworthy evidence synthesis in social care to inform and guide policy and practice. We articulate INSIA's position in the global field of social intervention assessment, list the vision and aims of INSIA as a network for promoting and enabling the production and use of evidence synthesis in social care, and issue a call to action, inviting other organizations to join this work.

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)0.017
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0130.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.758
GPT teacher head0.789
Teacher spread0.031 · 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