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Record W2889814721 · doi:10.1177/1088357618800061

Prevalence and Frequency of Online Sexual Activity by Adults With Autism Spectrum Disorder

2018· article· en· W2889814721 on OpenAlexaff
E. Sandra Byers, Shana Nichols

Bibliographic record

VenueFocus on Autism and Other Developmental Disabilities · 2018
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPsychologyHuman sexualityAutism spectrum disorderSexual identityArousalClinical psychologySexual arousalDevelopmental psychologyAutismYoung adultSexual behaviorSocial psychology

Abstract

fetched live from OpenAlex

We examined the prevalence and frequency with which cognitively able adults (141 men, 190 women) with autism spectrum disorder (CA-ASD) engaged in a range of online sexual activities (OSAs). Participants completed an online survey that assessed their recent involvement in nonarousal (Information Seeking, Chatting), solitary-arousal (S-OSA), and partnered-arousal (P-OSA) online sexual activities. Almost two thirds had engaged in one or more OSA but, on average, had done so infrequently. There were only a few differences based on sex, age, and sexual identity. Significantly more men than women had engaged in Information Seeking and S-OSA and had done so more frequently. Individuals in their 20s were significantly more likely to have engaged in Information Seeking. Sexual-minority individuals were more likely to report engaging in P-OSA than were heterosexual individuals. These results are discussed in terms of their implications for sexuality education aimed at assisting adults with CA-ASD to establish a healthy and meaningful sexuality.

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)0.002
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.264
Teacher spread0.246 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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