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Gender Differences in Stress Resistance of Adolescents with Autism Spectrum Disorders

2022· article· en· W4293802467 on OpenAlexvenueno aff
Iryna P. Yakymchuk, Iryna Vakhotska, Kateryna Radzivil, Kateryna Ivashchenko, Вікторія Полєхіна

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychology of Development and Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAutismCoping (psychology)Developmental psychologyInterpersonal communicationClinical psychologyEmpirical researchQualitative researchSocial psychology

Abstract

fetched live from OpenAlex

Objective: The research provided the empirical study of the gender differences in the experience of stress and coping with it in boys and girls diagnosed with high-functioning autism through qualitative methods. Methods: Observation was the main method used in the study, which was implemented by filling in the observation diary by specialists and parents of adolescents with autistic disorders. The main coping strategies under research were addressing others, avoiding the problem, and working on the problem. Content analysis was used as the main data processing method. Results: The study found some gender differences in coping with stress between males and females. Besides, it was found that the use of certain forms of coping behavior differs depending on age. Conclusions: The study allowed us to determine that boys tend to choose a strategy of working on the problem in interpersonal interaction, while girls are disposed to avoid the problem. The opposite picture was observed for the social environment: girls with ASD tend to use a strategy of addressing others, and boys avoid the problem through self-isolation.

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 score0.003
Threshold uncertainty score0.009

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.303
Teacher spread0.260 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueJournal of Intellectual Disability - Diagnosis and TreatmentSame topicPsychology of Development and EducationFrench-language works237,207