Co-creating a Canadian autism mental health literacy resource: a qualitative analysis of advisory perspectives
Bibliographic record
Abstract
BACKGROUND: Autistic adults experience disproportionately high rates of mental health challenges and encounter substantial barriers to care. While initiatives aimed at improving mental health literacy (MHL) offer one strategy for addressing these disparities, the processes through which such initiatives are co-produced with autistic adults and caregivers remain underexplored. Co-production - the collaborative development of resources or knowledge between researchers and community members - can enhance the relevance, authenticity, and impact of health initiatives. The central aim of this study was to understand how autistic adults and caregivers experienced their involvement in the co-production of an applied health research initiative. To inform future initiatives, there is a need to understand stakeholder experiences of the co-production process. METHODS: This study examined the experiences of stakeholders engaged in the co-production of a Canadian MHL resource for autistic adults and their families. Although the context of the project focused on MHL, the central aim was to understand how autistic adults and caregivers experienced their involvement in the co-production process. Semi-structured interviews were conducted with 24 autistic adults and caregivers who served as advisors in the Autism Mental Health Literacy Project (AM-HeLP). A reflexive thematic analysis approach was used to identify key experiential themes related to their involvement. RESULTS: A thematic analysis identified four main stakeholder experience themes: (1) the elements of co-production, (2) the collaboration process, (3) insights gained and (4) emotional impact of involvement. CONCLUSIONS: These findings highlight the critical importance of intentional, inclusive and trauma-informed co-production practices in applied health research. They offer practical guidance for researchers, service providers and policymakers seeking to authentically engage autistic adults and families in the development of health-related resources. Supporting equitable partnerships with autistic adults and caregivers is essential to advancing responsive and person-centred health policy and practice.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | high |
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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.028 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".