Exploring the impact of engagement in mental health and substance use research: A scoping review and thematic analysis
Bibliographic record
Abstract
BACKGROUND: There is growing evidence demonstrating the impact of engaging people with lived experience (PWLE) in health research. However, it remains unclear what evidence is available regarding the impact of engagement specific to mental health and substance use research. METHODS: A scoping review of three databases and thematic analysis were conducted. Sixty-one articles that described the impact of engagement in mental health and substance use research on either individual experiences or the research process were included. RESULTS: Key topics include (a) the impact of engagement on individual experiences; (b) the impact of engagement on the research process; and (c) facilitators and barriers to impactful engagement. Studies largely focused on the perceived positive impact of engagement on PWLE (e.g., personal and professional growth, empowering and rewarding experience, feeling heard and valued), researchers (e.g., rewarding experience, deeper understanding of research topic, changes to practice), and study participants (e.g., added value, fostered a safe space). Engagement activities were perceived to improve facets of the research process, such as improvements to research quality (e.g., rigour, trustworthiness, relevance to the community), research components (e.g., recruitment), and the research environment (e.g., shifted power dynamics). Facilitators and barriers were mapped onto the lived experience, researcher, team, and institutional levels. Commonly used terminologies for engagement and PWLE were discussed. CONCLUSION: Engaging PWLE-from consultation to co-creation throughout the research cycle-is perceived as having a positive impact on both the research process and individual experiences. Future research is needed to bring consistency to engagement, leverage the facilitators to engagement, and address the barriers, and in turn generate research findings that have value not only to the scientific community, but also to the people impacted by the science. PATIENT OR PUBLIC CONTRIBUTION: PWLE were engaged throughout the scoping review process, including the screening phase, analysis phase, and write-up phase.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | MetaresearchOpen science Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.134 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.053 | 0.054 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".