Designing in highly contentious areas: Perspectives on a way forward for mental healthcare transformation
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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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 | 0.076 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.022 | 0.084 |
| Scholarly communication | 0.028 | 0.024 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".