Discussing the gaps in the science and practice of lived experience engagement in mental health and substance use research: results of knowledge mobilization activities
Bibliographic record
Abstract
BACKGROUND: Engaging people with lived experience of mental health or substance use challenges and family members (PWLE) improves the quality and relevance of the associated research, but it can be challenging to include them meaningfully and authentically in the work. KNOWLEDGE MOBILIZATION EVENTS: After reviewing the literature on the science of lived experience engagement, we held two knowledge mobilization events to translate the findings to relevant partners and collect their feedback to guide our future research. A total of 55 people attended, bringing the perspective of people with lived experience, family members, research staff, research trainees, and scientists, as well as attendees holding multiple roles. We presented the scoping review findings, then held discussions to solicit feedback and encourage the sharing of perspectives. ATTENDEE PERSPECTIVES: Through small and large group discussion activities, we found that our scoping review findings resonated with the attendees' personal experiences with engagement in mental health and substance use research. Among the gaps highlighted in the discussions, the two that were most emphasized were the critical importance of improving diversity in engagement work in mental health and substance use, and the importance of addressing gaps around communication, relationships, rapport, and power dynamics in engagement spaces. CONCLUSIONS: Diversity, communication, relationships, and power dynamics emerge as key areas of work needed in the near future to advance the science of PWLE engagement in mental health and substance use research. We commit to pursuing the work that is considered of greatest need by a range of partners this research engagement sphere. We call on researchers in this area to continue this line of work, with a focus on the areas of research identified by attendees.
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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 | 0.263 | 0.409 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.005 | 0.048 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".