What Do We Know About Suicidality in Autism Spectrum Disorders? A Systematic Review
Bibliographic record
Abstract
Suicidality is a common and concerning issue across development, and there is a plethora of research on this topic among typically developing children and youth. Very little is known, however, about the nature of suicidality among individuals with autism spectrum disorders (ASDs). The purpose of the current study was to undertake a systematic literature review to assess the current state of the research literature to examine the prevalence of suicidality among individuals with ASD, related demographic and clinical profiles, and associated risk and protective factors. A literature search using key terms related to suicidality and ASD yielded 10 topical studies that were evaluated for the study objectives. Suicidality was present in 10.9-50% of the ASD samples identified in the systematic review. Further, several large-scale studies found that individuals with ASD comprised 7.3-15% of suicidal populations, a substantial subgroup. Risk factors were identified and included peer victimization, behavioral problems, being Black or Hispanic, being male, lower socioeconomic status, and lower level of education. Only one study reported on protective factors, and this is identified as a significant gap in the literature. Several methodological weaknesses were present in the current literature, such as lack of appropriate comparison groups and little to no use of empirically validated measures for ASD diagnosis and suicide assessment. Additional research is necessary to understand better how this unique population experiences and expresses suicidal tendencies. Recommendations for future research are discussed.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".