Engagement of people with lived experience in studies published in high-impact psychiatry journals: meta-research review
Bibliographic record
Abstract
BACKGROUND: We evaluated studies published in high-impact psychiatry journals to assess (1) the proportion that reported in articles whether they engaged people with lived experience; (2) the proportion that likely engaged people with lived experience; and, if engagement occurred, (3) stages of research (planning, conduct, interpretation, dissemination); and (4) engagement level (consult, involve, partner). METHODS: We searched PubMed on December 14, 2022, for articles in psychiatry journals with impact factor ≥ 10 and reviewed articles in reverse chronological order until 141 were included, based on pre-study precision estimation. We contacted authors to obtain information on engagement. RESULTS: Three of 141 (2%) studies reported engagement of people with lived experience in articles. Of the other 138 studies, 74 authors responded to follow-up emails and 22 reported they engaged people with lived experience but did not report in the article. Depending on assumptions about engagement by non-responders, we estimated, overall, 18-31% of studies may have engaged people with lived experience. Engagement occurred in research planning (70%) and rarely interpretation (35%). Most involved consultation (providing opinions or perspectives, 53%) and few involved partnership (11%). CONCLUSIONS: Engagement of people with lived experience in psychiatry research is uncommon, and when it does occur people are typically consulted but not engaged in roles with influence on decision-making. Funding agencies, ethics committees, journals, and academic institutions should take steps to support engagement of people with lived experience in psychiatry research.
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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 | 0.092 | 0.314 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".