A Qualitative Study of Self and Caregiver Perspectives on How Autistic Individuals Cope With Trauma
Bibliographic record
Abstract
Background: Coping can moderate the relationship between trauma exposure and trauma symptoms. There are many conceptualisations of coping in the general population, but limited research has considered how autistic individuals cope, despite their above-average rates of traumatic exposure. Objectives: To describe the range of coping strategies autistic individuals use following traumatic events. Methods: Fourteen autistic adults and 15 caregivers of autistic individuals, recruited via stratified purposive sampling, completed semi-structured interviews. Participants were asked to describe how they/their child attempted to cope with events they perceived as traumatic. Using an existing theoretical framework and reflexive thematic analysis, coping strategies were identified, described, and organized into themes. Results: Coping strategies used by autistic individuals could be organized into 3 main themes: (1) Engaging with Trauma, (2) Disengaging from Trauma, and (3) Self-Regulatory Coping. After the three main themes were developed, a fourth integrative theme, Diagnostic Overshadowing, was created to capture participants' reports of the overlap or confusion between coping and autism-related behaviors. Conclusions: Autistic individuals use many strategies to cope with trauma, many of which are traditionally recognized as coping, but some of which may be less easily recognized given their overlap with autism-related behaviors. Findings highlight considerations for conceptualizing coping in autism, including factors influencing how individuals cope with trauma, and how aspects of autism may shape or overlap with coping behavior. Research building on these findings may inform a more nuanced understanding of how autistic people respond to adversity, and how to support coping strategies that promote recovery from trauma.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".