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Record W4308550683 · doi:10.1080/09638237.2022.2140788

Evaluating recovery colleges: a co-created scoping review

2022· article· en· W4308550683 on OpenAlexafffundabout
Elizabeth Lin, Holly Harris, Georgia Black, Gail Bellissimo, Anna Di Giandomenico, Terri Rodak, Kenya A. Costa-Dookhan, Rowen Shier, Jordana Rovet, Sam Gruszecki, Sophie Soklaridis

Bibliographic record

VenueJournal of Mental Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of TorontoOntario Shores Centre for Mental Health SciencesCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsPsychologyMedical educationNursingMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Recovery Colleges (RCs) are education-based centres providing information, networking, and skills development for managing mental health, well-being, and daily living. A central principle is co-creation involving people with lived experience of mental health/illness and/or addictions (MHA). Identified gaps are RCs evaluations and information about whether such evaluations are co-created. AIMS: We describe a co-created scoping review of how RCs are evaluated in the published and grey literature. Also assessed were: the frameworks, designs, and analyses used; the themes/outcomes reported; the trustworthiness of the evidence; and whether the evaluations are co-created. METHODS: We followed Arksey and O'Malley's methodology with one important modification: "Consultation" was re-conceptualised as "co-creator engagement" and was the first, foundational step rather than the last, optional one. RESULTS: Seventy-nine percent of the 43 included evaluations were peer-reviewed, 21% grey literature. These evaluations represented 33 RCs located in the UK (58%), Australia (15%), Canada (9%), Ireland (9%), the USA (6%), and Italy (3%). CONCLUSION: Our findings depict a developing field that is exploring a mix of evaluative approaches. However, few evaluations appeared to be co-created. Although most studies referenced co-design/co-production, few described how much or how meaningfully people with lived experience were involved in the evaluation.

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 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)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.589
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.439
GPT teacher head0.586
Teacher spread0.147 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreEmpirical

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

Citations37
Published2022
Admission routes3
Has abstractyes

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