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Record W4243337749 · doi:10.31234/osf.io/qsfdj

Changes in Autistic Symptoms and Adaptive Functioning of Children Receiving Early Behavioral Intervention in a Community Setting: A Latent Growth Curve Analysis

2021· preprint· en· W4243337749 on OpenAlexafffund
Isabelle Préfontaine, Julien Morizot, Marc J. Lanovaz, Mélina Rivard

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversité du Québec à MontréalInstitut Universitaire en Santé Mentale de QuébecUniversité de Montréal
FundersMinistère de la SantéMinistère de la Santé et des Services sociaux
KeywordsIntervention (counseling)AutismAdaptive behaviorAdaptive functioningPsychologyClinical psychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Despite showing robust effects in well-controlled studies, the extent to which early intensive behavioral intervention produces positive changes in naturalistic, community-based settings remains uncertain. Thus, our study examined changes in autistic symptoms and adaptive functioning in 233 children in children with autism receiving early behavioral intervention in a community setting. The results revealed nonlinear changes in adaptive functioning characterized by significant improvements during the intervention period and a small linear decrease in autistic symptoms from baseline to follow-up. The intensity of intervention, age at enrolment, IQ and autistic symptoms were associated either with progress during the intervention period or maintenance during the follow-up period. Taken together, these results underline the importance of conducting further replications in community settings.

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)0.007
metaresearch head score (Gemma)0.016
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.048
GPT teacher head0.319
Teacher spread0.271 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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