Youth engagement in mental health research: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Patient engagement in youth mental health research has the potential to inform research on the interventions, services and policies that will benefit youth. At present, there is little evidence to guide mental health researchers on youth engagement. This systematic review aims to describe the impacts of youth engagement on mental health research and to summarize youth engagement in mental health research. METHODS: We searched the following databases: MEDLINE, EMBASE and PsycINFO, using a combination of subject headings, keywords and synonyms for the concepts 'patient engagement', 'youth' and 'mental health'. Articles that described engaging youth in mental health research were included. Two reviewers performed the study selection. Study characteristics, research activities performed by youth, impacts of youth engagement, challenges, and facilitators to engagement and recommendations for youth engagement described by authors were extracted. Quality appraisal involved determining the level of engagement of youth and the stage(s) of research where youth were involved. RESULTS: The database search returned 2836 citations, 151 full-text articles were screened and 16 articles, representing 14 studies, were selected for inclusion. Youth were involved at nearly all stages of the research cycle, in either advisory or co-production roles. Youth engagement impacts included enhancing relevant research findings, data collection and analysis and dissemination to academic and stakeholder audiences. Both youth and academic researchers reported personal development across many domains. One negative impact reported was the increase in funding and resources needed for engagement. We produced a list of 35 recommendations under the headings of training, youth researcher composition, strategy, expectations, relationships, meeting approaches and engagement conditions. CONCLUSIONS: This study provides an understanding of the impacts and recommendations of youth engagement in mental health research. The findings from this study may encourage researchers to engage youth in their mental health research and support youth engagement in funding applications. PATIENT AND PUBLIC CONTRIBUTION: We consulted three youths with experience being engaged in mental health research about the review findings and the discussion. One youth designed a visual representation of the results and provided feedback on the manuscript. All youth's input informed the way the findings were presented and the focus of the discussion.
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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 | 0.042 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".