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Record W2755471566 · doi:10.1097/dbp.0000000000000500

Mental Health Service Use Among Youth with Autism Spectrum Disorder: A Comparison of Two Age Groups

2017· article· en· W2755471566 on OpenAlexafffund
Stephanie Ryan, Jonathan Lai, Jonathan A. Weiss

Bibliographic record

VenueJournal of Developmental & Behavioral Pediatrics · 2017
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsYork University
FundersCanadian Institutes of Health ResearchPublic Health Agency
KeywordsMental healthReceiptAutism spectrum disorderAutismClinical psychologyPsychiatryMental health servicePsychologyMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Although youth with autism have elevated rates of mental health problems compared to typically developing youth, little is known about the mental health services that they receive. The current study examines predisposing, enabling, and clinical need factors as they relate to mental health service use in youth with autism. METHODS: The current study surveyed parents of 2337 children and adolescents with autism, compared their access to behavioral management and mental health treatment (MHT), and isolated the correlates of such receipt. RESULTS: Children used behavioral management more than adolescents, whereas the opposite was true for MHT. Mental health treatment receipt was associated with caregiver-related and mental health problems in both age groups, with routine health service use in children and with behavioral problems in adolescents. Behavioral management was correlated with caregiver-related services and behavioral problems in both age groups, and with sex and intellectual disability in adolescents. CONCLUSION: Clinical needs and caregiver service use are consistently associated with mental health care across ages, whereas the role of youth characteristics is particularly relevant when considering service use for adolescents.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.089
GPT teacher head0.367
Teacher spread0.278 · 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 teacher head, not a consensus.

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

Citations13
Published2017
Admission routes2
Has abstractyes

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