Applying experience-based co-design with vulnerable populations: Lessons from a systematic review of methods to involve patients, families and service providers in child and youth mental health service improvement
Bibliographic record
Abstract
The objective was to identify methods used to involve patients, family and service providers in child and youth mental health service improvement research. We analyzed the alignment of methods used with Experience-Based Co-Design (EBCD) methodology, and how power imbalances among participants were addressed. A systematic review of the English-language peer review literature since 2004 was carried out. The EMBASE, Scholar’s Portal, PubMed, Web of Science databases and the Ontario College of Art and Design University libraries were searched electronically for variations of ‘child’, ‘mental health’, ‘experience-based co-design’, ‘participatory research’ and ‘health care services’. Textual data was systematically extracted and analyzed. The electronic search identified 1468 articles; 13 remained following full text review and reference checking. Many participatory research studies in child and youth mental health were consistent with core elements of the EBCD methodology, but few focused on experiences and incorporated the perspectives of all participants throughout the research process. Story telling and visual media, employing youth as researcher partners, establishing equal status among participants, offering counseling support, paying particular attention to confidentiality, scheduling frequent breaks, and having skilled interviewers and facilitators were suggested methods to address power imbalances for this vulnerable population. Conclusion-The existing child and youth mental health participatory research literature aligns considerably with many elements of EBCD methodology and suggests diverse approaches to address power imbalances. More systematic application of the full range of elements will help to achieve patient centeredness and recovery in mental health and for other vulnerable populations.
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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 | 0.414 | 0.493 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".