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Record W2802963031 · doi:10.3389/fpsyt.2018.00203

Victimization and Perpetration Experiences of Adults With Autism

2018· article· en· W2802963031 on OpenAlexafffund
Jonathan A. Weiss, Michelle A. Fardella

Bibliographic record

VenueFrontiers in Psychiatry · 2018
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsYork University
FundersCanadian Institutes of Health ResearchHealth CanadaSinneave Family FoundationAutism Speaks
KeywordsAutismPsychologyClinical psychologyAutism spectrum disorderPsychiatryDevelopmental psychology

Abstract

fetched live from OpenAlex

This study aimed to describe the self-reported experiences of childhood and adult victimization and perpetration in adults with autism spectrum conditions (ASC) compared to a matched sample, and how victimization and perpetration are associated with autism-related difficulties. Forty-five adults with ASC and 42 adults without ASC completed questionnaires regarding violence victimization and perpetration, emotion regulation, and sociocommunicative competence. Participants with ASC reported experiencing, as children, more overall victimization; specifically, more property crime, maltreatment, teasing/emotional bullying, and sexual assault by peers, compared to participants without ASC. Participants with ASC also reported experiencing more teasing/emotional bullying in adulthood and greater sexual contact victimization. No significant differences were found between groups on perpetration. Sociocommunicative ability and emotion regulation deficits did not explain the heightened risk for victimization. Individuals with ASC have an increased vulnerability to violence victimization, which speaks to the need for interventions, and proactive prevention strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.248
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations156
Published2018
Admission routes2
Has abstractyes

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