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Record W2101082256 · doi:10.1111/fare.12081

Factors Associated with Caregiver Burden Among Parents of Individuals with <scp>ASD</scp>: Differences Across Intellectual Functioning

2014· article· en· W2101082256 on OpenAlexaff
Vanessa M. Vogan, Johanna Lake, Jonathan A. Weiss, Suzanne Robinson, Ami Tint, Yona Lunsky

Bibliographic record

VenueFamily Relations · 2014
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsYork UniversityUniversity of TorontoCentre for Addiction and Mental Health
FundersCenters for Disease Control and Prevention
KeywordsAutism spectrum disorderIntellectual disabilityCaregiver burdenComorbidityPsychologyAutismYoung adultMental healthClinical psychologyPsychiatryDevelopmental psychologyMedicineDisease

Abstract

fetched live from OpenAlex

Symptoms of autism spectrum disorder ( ASD ) persist into adolescence and adulthood, when access to health services and supports become difficult. Consequently, most adolescents and adults with ASD remain reliant on their families for support, often resulting in caregiver burden among parents. This study aims to investigate factors associated with burden in parents of adolescents and young adults with ASD , and to understand how these factors differ across varying levels of intellectual functioning. Of the 297 parents sampled, ASD severity, externalizing behaviors, medical comorbidity, and parent age predicted burden in parents of adolescents and young adults with ASD and an intellectual disability ( ID ), whereas an inability to pay for services predicted burden in parents of individuals with ASD and no ID . Factors associated with caregiver burden differed among individuals with and without ID and were not limited to symptom severity or mental health problems, but also extended to system factors.

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)0.001
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.074
GPT teacher head0.314
Teacher spread0.239 · 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

Citations38
Published2014
Admission routes1
Has abstractyes

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