Gaps in the Engagement of People With Lived and Living Experience and Caregivers in Mental Health and Substance Use Health Research: A Qualitative Study of Untapped Potential
Bibliographic record
Abstract
BACKGROUND: People with lived/living experience and family/caregivers (PLLEX-C) can be engaged in mental health and substance use health research in roles such as advisors, collaborators, and co-researchers. While there is a substantial body of research describing the barriers and facilitators to effective lived experience engagement, the actual contributions that PLLEX-C are making to the research remains under-explored. This qualitative descriptive study aimed to explore new areas where PLLEX-C can contribute to the research process. We wanted to understand gaps in the contributions of PLLEX-C and how we can provide opportunities to grow and enhance their contributions to the research in which they are engaged. METHODS: A Canada-wide sample of 28 PLLEX-C took part in one of five focus groups, while 12 researchers from across Canada took part in individual interviews using a co-designed semi-structured interview guide. Discussions were recorded, transcribed, and analyzed using codebook thematic analysis. We engaged PLLEX-C throughout the course of the study. RESULTS: Gaps in the engagement of PLLEX-C were found across the research lifecycle. This included key aspects of project initiation, like establishing research questions and priorities, contributing to grant applications, and contributing to ethics processes. Gaps were also encountered in the research operations process, in terms of recruitment processes and data analysis. Lastly, gaps at the end-of-grant knowledge translation stage included manuscript co-authorship and co-presentation at conferences or other events. CONCLUSIONS: PLLEX-C are willing to be engaged in research across the research lifecycle, but many have experienced areas of untapped potential. To develop appropriate engagement plans for a given project, it is important to have open discussions with the PLLEX-C engaged to understand their areas of skill, interest, and professional development goals, as well as barriers to full engagement in some stages of the project. This will make it possible to co-design a creative and flexible personalized engagement plan that is meaningful to them and maximizes their engagement potential. This process will ensure that authentic engagement overrides tokenistic practices. PATIENT ENGAGEMENT: This study was conducted on lived/living experience engagement and the team includes people with lived/living experience. A lived/living experience working group contributed to the design and operation of the project, as well as to data analysis, interpretation, and co-authorship.
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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 | 0.036 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.027 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".