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Record W4310773802 · doi:10.1177/23969415221138699

Quickstart for toddlers with autism spectrum disorder: A preliminary report of an adapted community-based early intervention program

2022· article· en· W4310773802 on OpenAlexaff
Robin Gaines, Yolanda G. Korneluk, Danielle Quigley, Véronique Chiasson, Abigail Delehanty, Suzanne Jacobson

Bibliographic record

VenueAutism & Developmental Language Impairments · 2022
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsAutism CanadaDouglas CollegeChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsAutism spectrum disorderIntervention (counseling)AutismPsychologySpectrum (functional analysis)Developmental psychologyClinical psychologyPsychiatryPhysics

Abstract

fetched live from OpenAlex

Background and Aims Early intervention (EI) for young children with autism spectrum disorder (ASD) must be resource-efficient while remaining effective; thus, clinicians are challenged to create and implement useful methods. Clinical evidence from community-based interventions that include reliable diagnoses, individual EI programs, along with comprehensive descriptions of participants, procedures, and participant outcomes can inform practice, translational research, and local policy. Parent-mediated EI for toddlers with ASD can promote positive developmental outcomes and lifelong well-being, but evidence of successful community uptake of research-based EIs is somewhat limited. The community-based, parent-mediated, evidence-informed QuickStart EI program aims to encourage toddlers’ early social communication, social interactions, and relationship-building, in a community clinic setting. We aim to (1) describe our adaptations to the evidence-based Parent-Delivered Early Start Denver Model and (2) present promising findings for toddlers with or at risk for ASD and their families who received QuickStart. We also intend to motivate a similar study of EI in real-world situations to advance evidence-based practice and create relevant dialogue and questions for research. Methods Complete data were identified and analyzed for up to 89 toddlers diagnosed with, or at risk of, ASD. Pre- and post-intervention parent- or self-report data were analyzed using descriptive statistics and paired-sample t-tests, as appropriate. Pre-intervention measures included demographic information ( n = 89) and the Early Screening of Autism and Communication (ESAC; n = 89). Measures taken pre- and post-intervention included the Adaptive Behavior Assessment System-II ( n = 60), MacArthur-Bates Communication Development Inventories ( n = 58), and the parental sense of competence scale ( n = 62). The Measure of Processes of Care ( n = 60) was taken post-intervention. On enrollment, parents signed standard clinical agreements that included statements allowing their anonymous data to be analyzed for research. Results Using standardized parent/self-report measures, toddler gains were noted for social interaction, language, communication skills, and ASD symptoms, but not for parents’ feelings of competence. Parents identified QuickStart procedures as family centered (Measure of Processes of Care). Conclusions The QuickStart EI program, provided to toddlers and their families over 20 weeks in a community clinic, resulted in promising positive behavior and communication changes, as indicated on the parent-response measures, for a moderately large sample of toddlers. Implications This study adds to the literature by describing a new EI program with clear procedures by which clinicians can create, provide, and evaluate a readily accessible, community-based EI for toddlers with or at risk of ASD. Methodological limitations inherent to our study design that precluded a control group and necessitated a reliance on available parent-report data are carefully critiqued and discussed.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.339
Teacher spread0.314 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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