Co‐designing for quality: Creating a user‐driven tool to improve quality in youth mental health services
Bibliographic record
Abstract
BACKGROUND: Although high quality mental health care for children and youth is a goal of many health systems, little is known about the dimensions of quality mental health care from users' perspectives. We engaged young people, caregivers and service providers to share experiences, which shed light on quality dimensions for youth mental health care. METHODS: Using experience-based co-design, we collected qualitative data from young people aged 16-24 with a mental disorder (n = 19), identified caregivers (n = 12) and service providers (n = 14) about their experiences with respect to youth mental health services. Experience data were collected using multiple approaches including interviews, a suite of online and smartphone applications (n = 22), and a co-design event (n = 16) and analysed to extract touch points. These touch points were used to prioritize and co-design a user-driven prototype of a questionnaire to provide feedback to service providers. FINDINGS: Young people, caregiver and service provider reports of service experiences were used to identify aspects of care quality at eight mental health service contact points: Access to mental health care; Transfer to/from hospital; Intake into hospital; Services provided; Assessment and treatment; Treatment environment; and Caregiver involvement in care. In some cases, low quality care was harmful to users and their caregivers. Young people co-designed a prototype of a user-driven feedback questionnaire to improve quality of service experiences that was supported by service providers and caregivers at the co-design event. CONCLUSION: By using EBCD to capture in-depth data regarding experiences of young people, their caregivers and service providers, study participants have begun to establish a baseline for acceptable quality of mental health care for young people.
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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 | 0.033 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".