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Record W4394820969 · doi:10.7870/cjcmh-2024-001

Framing Service User Involvement in Mental Health: A Qualitative Review

2024· review· en· W4394820969 on OpenAlexaffvenue
Cara Evans, Janelle Panday, Heather L. Bullock, Mary Anne Levasseur, Christopher Canning, Laura Tripp, Julia Abelson, Meredith Vanstone

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2024
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsWaypoint Centre for Mental Health CareUniversité du Québec à RimouskiMcMaster University
Fundersnot available
KeywordsGeneral partnershipFraming (construction)Mental healthService providerPublic relationsMental health serviceService (business)PsychologyMedicinePolitical sciencePsychiatryEngineeringBusinessMarketing

Abstract

fetched live from OpenAlex

Partnering with patients and family caregivers (commonly referred to as patient partnership) is increasingly common in health services, research, education, and policy. In the field of mental health, service user involvement intersects with distinct historical trajectories and as such, may take on unique forms. This review draws on a broader systematic review of literature on patient partnership. We ask: How does literature on patient partnership in mental health and addictions describe the history of service user involvement and the roles of service users? Two broad frames for service user involvement are identified, which offer contrasting perspectives about the history, value, and power relations involved in service user involvement. Future research can consider implications of these perspectives, and opportunities for synthesis.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.031
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.012
Science and technology studies0.0020.002
Scholarly communication0.0030.004
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.562
GPT teacher head0.577
Teacher spread0.015 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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