Lost in translation: a narrative review and synthesis of the published international literature on mental health research and translation priorities (2011–2023)
Bibliographic record
Abstract
BACKGROUND: Priority setting in mental health research is arguably lost in translation. Decades of effort has led to persistent repetition in what the research priorities of people with lived-experience of mental ill-health are. AIM: This was a narrative review and synthesis of published literature reporting mental health research priorities (2011-2023). METHODS: A narrative framework was established with the questions: (1) who has been involved in priority setting? With whom have priorities been set? Which priorities have been established and for whom? What progress has been made? And, whose priorities are being progressed? RESULTS: Seven papers were identified. Two were Australian, one Welsh, one English, one was from Chile and another Brazilian and one reported on a European exercise across 28 countries (ROAMER). Hundreds of priorities were listed in all exercises. Prioritisation mostly occured from survey rankings and/or workshops (using dots, or post-it note voting). Most were dominated by clinicians, academics and government rather than people with lived-experience of mental ill-health and carer, family and kinship group members. CONCLUSION: One lived-experience research led survey was identified. Few studies reported lived-experience design and development involvement. Five of the seven papers reported responses, but no further progress on priorities being met was reported.
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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 | 0.058 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".