Designing in highly contentious areas: Perspectives on a way forward for mental healthcare transformation
Why this work is in the frame
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Bibliographic record
Abstract
There is growing interest in service design to support transformation in mental healthcare. Early research in this area has shown some promising results, but has also revealed the contentious nature of this work. A better understanding of the complexity of design in mental health is needed to support the development of approaches that are appropriate for this context. As such, the aim of this paper is to examine areas of contention and related strategies employed when designing for mental health transformation. To realize this aim, a qualitative multiple case study of ten service design initiatives in mental health contexts was conducted. The analysis revealed five interconnected contentious issues: organizational constraints; ensuring meaningful participation; culture clashes; power dynamics; and systems approaches. These contentious issues are detailed and related strategies from various cases are put forward, providing a rich foundation for the ongoing development of service design approaches in mental health.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it