Co‐Authoring and Reporting on Lived Experience Engagement in Mental Health and/or Substance Research: A Qualitative Study and Guidance Document
Bibliographic record
Abstract
INTRODUCTION: There is a move towards engaging people with lived experience and families (PWLE/F)-also referred to as PWLE/F engagement-in mental health and/or substance use research. However, PWLE/F engagement is inadequately reported on in mental health and/or substance use research papers. OBJECTIVE: To understand what PWLE/F and researchers perceive are important components to report on related to engagement in mental health and/or substance use research. METHODS: This study included a qualitative description study underpinned by pragmatism. Data were collected through virtual interviews with 13 PWLE/F and 12 researchers across Canada and analysed using template analysis. The results were used to develop a reporting guidance document for engagement in mental health and/or substance use research. RESULTS: The results from the template analysis were structured through the following themes: (1) establishing the need for a guidance document; (2) aspects of engagement to report and reflect on; (3) guidance around co-authorship with PWLE/F; (4) practical tips for reporting on engagement and (5) considerations for journals. Participants identified a need for tailored guidance that is flexible and reflective, yet can promote transparency, accountability and learning in the field. A reporting guidance document was developed for engagement in mental health and/or substance use research that balances flexibility and standardisation while incorporating reflection into reporting. Guidance around co-authorship with PWLE/F partners was also included. CONCLUSION: The guidance document is intended to be used as a roadmap to help guide authors to meaningfully write about engagement without the rigid boundaries of a reporting guideline. We encourage research teams that engage PWLE/F in mental health and/or substance use research to consider using the guidance document as they write up their work. PATIENT AND PUBLIC INVOLVEMENT: PWLE/F members were engaged throughout the study from conception to manuscript production. This included a PWLE partner on the doctoral committee and a Lived Experience Advisory Group consisting of two PWLE and one family partner.
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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 | 0.114 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| 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; 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".