Experience-based co-design of mental health services and interventions: A scoping review
Bibliographic record
Abstract
Experience-based co-design (EBCD) is a structured methodology of conducting healthcare quality improvement bringing together people with lived experience of a condition, families or carers, and healthcare service providers. EBCD has been applied to mental health and substance use (MHSU) settings. This scoping review aimed to: (a) synthesize the literature on the application of EBCD in the MHSU service sector; and (b) map out key adaptations made to the EBCD process, as well as the perceived impacts of the process and considerations unique to the MHSU sphere. A scoping review methodology was applied. Systematic searches for articles describing EBCD projects in MHSU were conducted across six bibliographic databases for literature published between 2013 and 2023, together with gray literature searches and reviews of reference lists. Records were screened for relevance, resulting in 24 included articles. Data were extracted in a spreadsheet and using qualitative data analysis software. Results are reported descriptively and in table format. EBCD is being conducted in the MHSU sector in a limited number of high-income, English-speaking countries, applied to both quality improvement and new intervention development. Extensive methodology adaptations are described, with some steps frequently removed from the process or modified. A number of positive impacts of EBCD are described, highlighting the development of service adaptation or new services, as well as positive interpersonal impacts among stakeholders. EBCD is a valuable approach to collaboratively co-designing quality improvement initiatives with users of MHSU services, families or carers, and service providers, although it is also applied to new intervention development. Those implementing EBCD should carefully consider the way planned adaptations may affect the information gathered, the implementation experience, and the co-designed solutions. It is important to apply trauma-informed practices to EBCD and follow recommendations for authentic engagement, to promote genuine participation in co-designing solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one teacher head, 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".