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Record W3080745040 · doi:10.1177/1362361320947325

Construct validity of the First-Year Inventory (FYI Version 2.0) in 12-month-olds at high-risk for Autism Spectrum Disorder

2020· article· en· W3080745040 on OpenAlexafffund
Helen Lee, Cheryl Vigen, Lonnie Zwaigenbaum, Isabel M. Smith, Jessica Brian, Linda R. Watson, Elizabeth R. Crais, Grace T. Baranek

Bibliographic record

VenueAutism · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of TorontoDalhousie UniversityUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsAutism spectrum disorderAutismPsychologyConstruct validityPervasive developmental disorderConstruct (python library)Test validityDevelopmental psychologyPsychometricsClinical psychology

Abstract

fetched live from OpenAlex

This study examines the construct validity of the First-Year Inventory 2.0 with respect to other established instruments in a sample of high-risk infant siblings of children with Autism Spectrum Disorder. The First-Year Inventory 2.0 is a parent-report screening instrument designed to identify 12-month-old infants at risk for an eventual diagnosis of Autism Spectrum Disorder and consists of two domains: Social-Communication and Sensory-Regulatory. Although the First-Year Inventory 2.0’s screening psychometrics have been examined, its construct validity has not been investigated. In a sample of 112 high-risk 12-month-olds, we examined the First-Year Inventory 2.0’s associations with the Autism Observation Scale for Infants, an observer-based Autism Spectrum Disorder screener, and with other developmental instruments measuring similar areas in social communication and regulatory functioning in young children. Findings generally supported the First-Year Inventory 2.0 associations with other instruments in expected ways. The First-Year Inventory 2.0 Social-Communication domain was notably associated with the Autism Observation Scale for Infant’s total score and with language and communication domains of the Vineland and the Mullen. The Sensory-Regulatory domain showed minimal associations with other instruments that only had a few sensory items. Considering different objectives and strengths of assessments, researchers and clinicians are encouraged to utilize a variety of instruments in a comprehensive evaluation of a child. Lay abstract The First-Year Inventory 2.0 is a parent-report screening instrument designed to identify 12-month-old infants at risk for an eventual diagnosis of Autism Spectrum Disorder. This instrument focuses on Social-Communication and Sensory-Regulatory areas of infant behavior. Although the First-Year Inventory 2.0 screening performance has been previously studied, its validity has not been examined. Establishing validity of an instrument is important because it supports the effectiveness and the reliability of the instrument. In this study, we examined relationship between the First-Year Inventory 2.0 (Social-Communication and Sensory-Regulatory areas) and other instruments that measure similar areas of infant behavior in a sample of high-risk infant siblings of children with Autism Spectrum Disorder. These other instruments share some common aims and theoretical areas with the First-Year Inventory 2.0: the Autism Observation Scale for Infants, the Mullen Scales of Early Learning, the Vineland Adaptive Behavior Scales-II, and the Infant Behavior Questionnaire. Findings generally supported the validity of the First-Year Inventory 2.0 with other instruments. In particular, the Social-Communication area of the First-Year Inventory 2.0 showed greater commonality with other instruments than in the Sensory-Regulatory area. The Sensory-Regulatory area seemed to be a unique feature of the First-Year Inventory 2.0 instrument. Considering different aims and strengths of assessments, researchers and clinicians are encouraged to utilize a variety of instruments in a comprehensive evaluation of a child.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.033
GPT teacher head0.261
Teacher spread0.228 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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