Three papers examining mental health service provision with autistic adults in Canada
Bibliographic record
Abstract
Autistic adults experience higher rates of mental health challenges than their allistic (non-autistic) peers, and face numerous barriers associated with inaccessible and ineffective mental health systems of care. These barriers compound adverse mental health outcomes, increasing vulnerability to burnout, suicide, and premature mortality, and result in a need for more intensive and extensive mental health supports. Autistic adults have identified a need for translational research to highlight opportunities to improve accessibility and efficacy of supports and promote autistic-affirming mental health research and practices. This dissertation comprises three papers that address these challenges and offer novel insights into the experiences of mental health and services among autistic adults and the mental health providers that support them. This project was guided by pragmatism, neurodiversity, and social work theories and the lived and professional experiences of the author and external project consultants. Interpretive description and participatory methods were used to generate applicable findings coherent with the lived experiences of autistic adults. Sixty-seven participants (41 autistic adult clients and 26 mental health providers) shared their experiences in an online asynchronous qualitative questionnaire. Findings shared across three papers highlight opportunities for enhancing and strengthening autistic-affirming mental health services and research. The first paper explores mental health experiences of autistic adults and offers an emerging theory of autistic mental health as “the place where autism, trauma, and mental health meet”. The second paper builds on this with an examination of experiences of support and offers an emerging framework that can be used to inform the development and enhancement of autistic-affirming policies and practices in mental healthcare. The third paper pivots to exploring experiences of online asynchronous participation in research and offers insight into the potential value of this method for increasing accessibility for autistic adults and mental health providers motivated to participate in research. Together, these papers contribute to scholarly knowledge and offer considerations and implications that can be used by clinicians, researchers, organizations, policy makers, and autistic advocates to inform the development and strengthening of autistic-affirming mental health research and practice initiatives.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.039 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".