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Record W2133848618 · doi:10.1177/1088357612472932

Home Sweet Home? Families’ Experiences With Aggression in Children With Autism Spectrum Disorders

2013· article· en· W2133848618 on OpenAlexaff
Sandy Thompson‐Hodgetts, David Nicholas, Lonnie Zwaigenbaum

Bibliographic record

VenueFocus on Autism and Other Developmental Disabilities · 2013
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsRespite careAggressionAutismPsychologyAutism spectrum disorderQualitative researchClinical psychologyDevelopmental psychologySelf-destructive behaviorGroup homePsychiatryPoison controlInjury preventionMedicineNursing

Abstract

fetched live from OpenAlex

Although not inherent to the diagnosis, many individuals with autism spectrum disorders (ASD) display aggressive behavior. This study examined the experiences of families living with individuals with ASD who also demonstrate aggressive behaviors. Using a qualitative approach, semistructured interviews were conducted with parents of nine males with autism and aggression. Eight families’ homes also were observed. Through constant-comparison analysis of interview data, triangulated with home observations, three central processes were identified: deleterious impact on daily routines and well-being of family members, limited supports and services, and financial strain. Emergent themes included isolation, exhaustion, safety concerns, home expenses, respite needs, and limited professional supports and alternative housing. Examination of families’ experiences living with someone with ASD who is aggressive, and the impact of aggression on the supports and services that families receive, constitutes an important step in tailoring resources to best meet families’ needs.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.004
Scholarly communication0.0020.002
Open science0.0010.004
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 head0.013
GPT teacher head0.258
Teacher spread0.245 · 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

Citations116
Published2013
Admission routes1
Has abstractyes

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