Co-designing Services for Youth With Mental Health Issues: Novel Elicitation Approaches
Bibliographic record
Abstract
Experience-based co-design (EBCD) is an innovative, evidence-based approach to health and social system change based on principles of participatory action research, narrative and learning theory, and design thinking. Unique elicitation strategies such as experience mapping, trigger videos, and prototype development are used in EBCD to engage service users and service providers in a collaborative process of identifying touchpoints and solutions to system-level problems. In this article, we present findings from interviewing a purposeful sample of 18 participants (4 youth, 6 service providers, 6 family members, and 2 employers) across three co-design projects designed to address either mental health or employment services for youth (aged 16–24) with mental health issues in one urban center. Through interviewing participants, perceptions were explored relating to three elicitation techniques: creating experience maps, creating and viewing trigger videos, and co-designing visual “prototype” solutions. Analysis of participants’ comments indicated that these techniques can be powerful tools to foster mutual understanding and collaborative ideas, but they require a social, spatial, and temporal context that optimizes their value. A “safe space” is needed within which the essential elements of elicitation—building trust, finding voice, sharing perspectives, and creating a common vision—can occur. Three core, overlapping processes of co-design elicitation were identified: “building common perspectives,” “building mutual understanding,” and “building innovation.” We present a conceptual framework depicting the interplay of processes and elicitation techniques, essential to building mutual understanding and innovation during the EBCD process.
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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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".