Traumatic Experiences, Psychological Distress and Suicide‐Related Behaviors in Autistic Adults
Bibliographic record
Abstract
Autistic adults have increased risks of trauma, suicide, and poor mental health compared to non-autistic adults, with 1 in 4 autistic adults attempting suicide. We administered an anonymized, self-report survey to 424 autistic and 345 non-autistic adults through a convenience sampling framework. Binomial logistic regression models identified whether trauma and autism diagnosis were related to (i) self-harm, (ii) suicide attempts, (iii) suicide plans, (iv) a mental health condition that impacts daily life, and (v) substance use to cope. Heatmaps were generated to identify traumas that frequently co-occur with psychological distress and SRB. After accounting for trauma and demographic differences, autism remained a significant predictor of all outcomes, except whether individuals used substances to cope (OR: 0.78, 95% CI: 0.54-1.12, p = 0.18). Autistic people were more likely to report self-harm (OR: 2.71, 95% CI: 1.85-4.00, p < 0.01), suicide attempts (OR: 2.45, 95% CI: 1.65-3.68, p < 0.01), suicide plans (OR: 2.00, 95% CI: 1.41-2.83, p < 0.01), and experiencing a mental health condition that impacts daily life (OR: 3.58, 95% CI: 2.42-5.33, p < 0.01) than non-autistic people. Among autistic people, childhood victimization co-occurred with a mental health condition that impacts daily life, self-harm, and suicide plans most frequently. This study provides evidence of complex relationships between autism, trauma, self-harm, suicide attempts, suicide plans, and a mental health condition that impacts daily life. Focusing on the prevention of trauma, coping strategies, and recovery from traumatic events through safeguarding and support may be critical tools for suicide prevention among autistic people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".