Finding harmony within dissonance: Engaging patients, family/caregivers and service providers in research to fundamentally restructure relationships through integrative dynamics
Bibliographic record
Abstract
BACKGROUND: Deeply divided ideological positions challenge collaboration when engaging youth with mental disorders, caregivers and providers in mental health research. The integrative dynamics (ID) approach can restructure relationships and overcome 'us vs them' thinking. OBJECTIVE: To assess the extent to which an experience-based co-design (EBCD) approach to patient and family engagement in mental health research aligned with ID processes. METHODS: A retrospective case study of EBCD data in which transitional-aged youth (n = 12), caregivers (n = 8) and providers (n = 10) co-designed prototypes to improve transitions from child to adult services. Transcripts from focus groups and a co-design event, co-designed prototypes, the resulting model, evaluation interviews and author reflections were coded deductively based on core ID concepts, while allowing for emergent themes. Analysis was based on pattern matching. Triangulation across data sources, research team, and youth and caregiver reflections enhanced rigour. FINDINGS: The EBCD focus group discussions of touchpoints in experiences aligned with ID processes of acknowledging the past, by revealing the perceived identity mythos of each group, and allowing expression of and working through emotional pain. These ID processes were briefly revisited in the co-design event, where the focus was on the remaining ID processes: building cross-cutting connections and reconfiguring relationships. The staged EBCD approach may facilitate ID, by working within one's own perspective prior to all perspectives working together in co-design. CONCLUSION: Researchers can augment patient engagement approaches by applying ID principles with staged integration of groups to improve relations in mental health systems, and EBCD shows promise to operationalize this.
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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 | 0.107 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.039 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| 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".