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Record W4403988922 · doi:10.1111/hex.70087

Lived Experience and Family Engagement in Mental Health and Substance use Health Research: Case Profiles of Five Studies

2024· article· en· W4403988922 on OpenAlexafffund
Lisa D. Hawke, Lena C. Quilty, Branka Agic, Darren Courtney, Gray Liddell, Etienne Sibille, Sheila Jennings, Joshua Orson, Holly Harris, Shelby McKee, Sophie Soklaridis, Tarek K. Rajji, Sanjeev Sockalingam

Bibliographic record

VenueHealth Expectations · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health ResearchCundill Centre for Child and Youth DepressionUniversity of TorontoDepartment of Psychiatry, University of TorontoCentre for Addiction and Mental Health FoundationFondation Brain Canada
KeywordsLived experienceMental healthSituatedPsychologyCommunity engagementPublic engagementInstitutionHealth careMedical educationNursingPublic relationsMedicineSociologyPolitical sciencePsychiatryPsychotherapistSocial science

Abstract

fetched live from OpenAlex

INTRODUCTION: People with lived and living experience (PWLLE) and family members (F) can engage in mental health and substance use health research beyond participant roles, as advisors, co-researchers, equal partners and research leads. However, implementing meaningful and effective engagement is complex. METHODS: This article profiles five research initiatives involving different lived experience engagement structures, situated in a single tertiary care teaching and research hospital. RESULTS: The profiled projects feature various study designs and stages, ranging from initial priority setting to implementation efforts. The levels of engagement range from consultation to PWLLE/F leadership. Across diverse populations, all embody high-quality engagement and illustrate that PWLLE/F can have an important impact on a wide range of mental health and substance use health research. CONCLUSIONS: Engagement can be implemented flexibly within a single research institution to meet a wide range of needs and preferences of researchers and PWLLE/F. PATIENT AND PUBLIC CONTRIBUTION: Each of the research initiatives profiled was conducted with substantial lived experience engagement, as described herein. People with lived and living experience from each research initiative are also included in the authorship team and contributed to this manuscript.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.758
GPT teacher head0.592
Teacher spread0.166 · 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 designQualitative
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

Citations1
Published2024
Admission routes2
Has abstractyes

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