Lived Experience and Family Engagement in Mental Health and Substance use Health Research: Case Profiles of Five Studies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".