Childhood adversity, resilience, and autism: a critical review of the literature
Bibliographic record
Abstract
The long-term, negative physical and mental health effects of childhood adversity are well-documented in the literature, as are the mitigating effects of resilience factors. However, for those on the autism spectrum, these phenomena are relatively unstudied and not well-understood. Articulating the concept of mental health as a function of childhood adversity, resilience, and autistic identity, provides a foundation from which to conduct research and provide clinical mental health supports to individuals on the autism spectrum. Research on adversity and resilience in this population must consider neurodiversity and foreground the perspectives of the autism and autistic communities in research design, study implementation, and findings dissemination. Points of interestChildren on the autism spectrum experience more adversity and more mental health challenges than their non-autistic peers, a relationship that is not well-understood by researchers or clinicians. Autistic adults can provide insight into their experience of adversity and the potential influence of it on their mental health.It is unclear what factors contribute to protecting autistic children from the potential negative effects of adversity. This protection is known as resilience and may be developed differently in children on the autism spectrum.Childhood adversity and resilience are important considerations in developing mental health supports for individuals on the autism spectrum.Research and clinical practice can adopt a neurodiversity-affirming perspective to better meet the mental health needs of autistic individuals.Engaging the autistic community to provide their perspectives and experiences is vital to developing effective mental health strategies for those on the autism spectrum.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".