Dissemination of study results to participants in mental health research: a meta-research review of studies published in high-impact psychiatry journals
Bibliographic record
Abstract
OBJECTIVES: We surveyed authors of publications in high-impact psychiatry journals to assess the (1) proportion that disseminated results to study participants or others with lived experience, and, among those who disseminated, (2) methods (eg, email) and (3) tools (eg, plain-language summary) used. DESIGN: Meta-research review. DATA SOURCE: PubMed search on 14 December 2022 and emails to study authors for information on dissemination. ELIGIBILITY CRITERIA: Eligible studies collected primary human data and were published in psychiatry journals with 2021 impact factor ≥10. DATA EXTRACTION AND SYNTHESIS: Study information was extracted by one investigator and validated by a second investigator, with conflicts resolved by consensus, with a third investigator consulted as necessary. We emailed authors approximately 2 years post-publication to ensure sufficient time had passed to share results. We estimated the proportion of authors that may have disseminated results to participants or others with lived experience, assuming that non-respondents (1) did not disseminate, (2) were half as likely to disseminate as respondents or (3) disseminated in the same proportion as respondents. RESULTS: Of 141 studies, 94 (67%) authors responded. Among respondents, 21 (22%) reported disseminating to study participants, and an additional 9 (10%) reported disseminating lay materials to people with lived experience (total of 30 studies, 32%). Overall, we estimated that 15% (95% CI 10% to 22%) to 23% (95% CI 17% to 30%) of authors may have disseminated results directly to study participants and 21% (95% CI 15% to 29%) to 32% (95% CI 25% to 40%) to participants or others with lived experience. Among the 30 that reported disseminating, the most common methods were sending mail or emails to study participants (17 studies, 57%) and posting on social media (15 studies, 50%). The most common tools were plain-language summaries (22 studies, 73%) and webinars or other meetings (15 studies, 50%). CONCLUSIONS: Dissemination of results to participants in mental health research is uncommon. Funding agencies, ethics committees, journals and academic institutions should support dissemination.
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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 | 0.245 | 0.513 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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; 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".