Examining the barriers and facilitators to mental health service provision for autistic people in Ireland: a survey of psychiatrists
Bibliographic record
Abstract
BACKGROUND: Autistic people have high levels of mental ill-health and an increased risk of suicide across the lifespan. Yet autistic people report difficulties communicating with healthcare professionals and accessing a range of healthcare services. At the same time, mental healthcare workers in other countries are reporting links between confidence when working with autistic patients and the degree of autism knowledge and training they can access. METHODS: We sought to examine what factors helped or hindered Irish mental healthcare colleagues when working with autistic healthcare service users. An online survey using quantitative and qualitative metrics was circulated among psychiatrists who are members of the College of Psychiatrists of Ireland, both in training and at consultant level, from April 2021 to April 2022. RESULTS: = 140), but self-efficacy scores were variable, particularly in relation to care pathways. Self-efficacy was better among psychiatrists with caseloads of children and youth or individuals with co-occurring intellectual disabilities. Three key qualitative themes emerged relating to capacity and training of mental health professionals, ways to improve mental health services provision for autistic individuals and also the critical need for co-creation and neurodiversity affirmative care. CONCLUSIONS: The study highlighted critical systemic and professional challenges in providing mental health care to autistic people in Ireland. We provide recommendations for reducing these challenges and for enabling the development of inclusive, evidenced-based care to autistic individuals.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".