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Record W2906213511 · doi:10.1080/24694193.2018.1559256

Having a Sibling with ASD: Perspectives of Siblings and Their Parents

2018· article· en· W2906213511 on OpenAlexaff
Monica L. Molinaro, Lauren E. Rollo, Paula C. Fletcher, Margaret A. Schneider

Bibliographic record

VenueComprehensive Child and Adolescent Nursing · 2018
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsWilfrid Laurier UniversityWestern University
Fundersnot available
KeywordsSiblingPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

The overall purpose of this research was to explore the experiences of families living with a child with autism spectrum disorder (ASD). This paper reports the experiences of siblings of children with ASD, from the perspective of both siblings and parents. Using a phenomenological case study design, participants completed face sheets to provide context for one-on-one, semi-structured interviews, which were transcribed verbatim, and verified via member checks. Van Manen's (1990) selective approach was used for data analysis. Siblings and parents described that the children with ASD made their siblings the targets of their aggression, and siblings spent less time with parents the children with ASD required more attention. It was also acknowledged that the siblings were more mature as a result of having a sibling with ASD. Families acknowledged that the relationship between children with ASD and their siblings would not differ if their children did not have ASD. This work highlights the importance of examining the family as a unit to provide a multifaceted perspective of how having a child with ASD affects their siblings.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.002
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 head0.038
GPT teacher head0.335
Teacher spread0.297 · 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 designQualitative
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

Citations17
Published2018
Admission routes1
Has abstractyes

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