A Scoping Review of Autistic Children’s Neurological, Cognitive, and Psychological Responses to Trauma
Bibliographic record
Abstract
Background: Research tells us that autistic children are more likely than their neurotypical peers to experience potentially traumatic events. There is, however, limited literature on how autistic children respond to trauma, and there are few tools available to specifically assess autistic children’s exposure and response to trauma. Consequently, clinicians may lack the knowledge or resources to adequately support these individuals and provide them with appropriate, non-harmful care. Methods: In this scoping review, four databases (CINAHL, MEDLINE, Emcare, and PsycINFO) were searched using keywords that represented the research question: “What are autistic children’s neurological, cognitive, and psychological responses to trauma?” Quantitative and qualitative analyses were conducted on the data, and a frequency analysis was conducted to categorize and describe the results. Results: Thirty-one studies were included in the final analysis. Eleven themes were identified, which included social anxiety, isolation and withdrawal, generalized anxiety, depression, negative emotions and feelings, posttraumatic stress disorder and intrusive thoughts, social behaviors and skills, and behavioral challenges. Conclusions: This scoping review provides important insights into the unique trauma responses of autistic children and highlights the need for additional research and evidence-based guidance to inform clinical best practices and trauma-informed care for this population.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".