Elevated Suicidal Thoughts and Behaviors and Nonsuicidal Self-Injury in Autistic Youth and Adults: A Multinational Study
Bibliographic record
Abstract
Background: Autistic people are at significant risk for suicidal thoughts and behaviors (STBs) and nonsuicidal self-injury (NSSI). We examined STB and NSSI in different age-groups, considering sex- and age-based effects, in a pooled multinational sample of English-speaking autistic children and adults. Methods: We administered the Columbia-Suicide Severity Rating Scale to 245 autistic people without intellectual disability (139 youth and 106 adults; 46.1% female sex; M age = 24.8, SD age = 16.3 years, range = 7–70) or their caregivers in Australia, Canada, and the United States. The study samples were enriched with autistic people experiencing depression and suicidality. Results: Most participants (87.8%) reported suicidal ideation and NSSI (56.3%). Nearly one-third of autistic people (31.0%) reported a lifetime suicide attempt ( M = 2.9 attempts; range = 1–26); overdosing was the most common method of suicide attempt. Sex was not a significant risk factor for suicidal ideation, behavior, or NSSI. Increases in lifetime suicidal ideation were observed across older age-groups, with those aged over 18 years reporting more severe and longer-lasting ideation than in children or adolescents. The youngest age of suicide attempt was 7 years in this sample, and the average ages of first/initial, most lethal, and most recent suicide attempts in youth ( n = 24) and adults ( n = 42) were 16.6, 19.2, and 20.8 years old, respectively. Conclusion: Regardless of age-group, autistic people across the lifespan constitute a high-priority group for suicide prevention strategies, development of appropriate assessments, and evaluation of system-level programs that effectively address the problem of preventable death by suicide.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".