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Record W4306664007 · doi:10.1002/bin.1917

Telehealth parent training for a young child at risk for autism spectrum disorder

2022· article· en· W4306664007 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBehavioral Interventions · 2022
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsBrock University
FundersCanadian Institutes of Health Research
KeywordsTelehealthAutism spectrum disorderParent trainingPsychologyIntervention (counseling)AutismPsychological interventionImitationClinical psychologyFidelityDevelopmental psychologyMultiple baseline designTelemedicinePsychiatryHealth care

Abstract

fetched live from OpenAlex

Abstract The global pandemic has highlighted the importance of telehealth to access behavioral interventions. Face‐to‐face parent training improves the development and behaviors of young children at risk for autism spectrum disorder (ASD). We evaluated a telehealth parent training intervention for a child at risk for ASD. Two parents identified possible early ASD symptoms in their 30‐month‐old son (lack of imitation, pointing, and vocal manding). Both parents simultaneously received telehealth behavioral skills training on the Parent Intervention for Children at Risk for Autism program for 1 hour per week over 29 weeks. Multiple baseline designs across parent and child behaviors showed that both parents improved their parent teaching fidelity above 80% and the child improved on all trained behaviors. This study expands the utility of telehealth behavioral parent training to young children at risk for ASD to mitigate early symptoms of ASD.

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.

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), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.175
GPT teacher head0.422
Teacher spread0.247 · 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