Lived experience engagement in mental health research: Recommendations for a terminology shift
Bibliographic record
Abstract
Engaging people with lived experience (PWLE) of mental health challenges is increasingly considered a priority in health research settings.1 Lived experience engagement involves integrating PWLE in the full range of research processes, in roles such as advisors, collaborators, co-researchers, or full partners.In these roles, PWLE can provide many contributions to research, from the earliest stages of identifying research questions to integrated and end-of-grant knowledge translation.2 There are published examples of PWLE engagement in a wide range of health research, across a diversity of study designs and research topics.2-4 Increasingly, engagement is being considered an ethical imperative and anti-oppressive practice, given the history and continuing experience of inequities in both research and clinical practices.5 PWLE engagement is a pragmatic yet emancipatory research activity, according to which lived experience Health Expectations.
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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 | 0.385 | 0.339 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.016 | 0.046 |
| Scholarly communication | 0.041 | 0.088 |
| Open science | 0.023 | 0.058 |
| Research integrity | 0.035 | 0.063 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".