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Record W2160231077 · doi:10.1177/1362361313518123

Examining the criterion-related validity of the Pervasive Developmental Disorder Behavior Inventory

2014· article· en· W2160231077 on OpenAlexaff
Carly A. McMorris, Adrienne Perry

Bibliographic record

VenueAutism · 2014
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsYork University
Fundersnot available
KeywordsPervasive developmental disorderAdaptive behaviorVineland Adaptive Behavior ScalePsychologyAutismAutism spectrum disorderRating scaleDevelopmental disorderCriterion validityTest validityChildhood Autism Rating ScaleConstruct validityDiscriminant validityDevelopmental psychologyClinical psychologyConcurrent validityValidityPsychometrics

Abstract

fetched live from OpenAlex

The Pervasive Developmental Disorder Behavior Inventory is a questionnaire designed to aid in the diagnosis of pervasive developmental disorders or autism spectrum disorders. The Pervasive Developmental Disorder Behavior Inventory assesses adaptive and maladaptive behaviors associated with pervasive developmental disorders and provides an age-standardized Autism Composite score. In previous research, the Pervasive Developmental Disorder Behavior Inventory has demonstrated moderate to strong reliability and validity. This study aimed to replicate and extend previous research by investigating the criterion-related validity of the Pervasive Developmental Disorder Behavior Inventory. Data from 40 children were analyzed in relation to other measures. The Pervasive Developmental Disorder Behavior Inventory adaptive scores were moderately correlated with cognitive and adaptive behavior scores as expected. However, no significant correlations were found between the maladaptive and Autism Composite scores of the Pervasive Developmental Disorder Behavior Inventory and the Childhood Autism Rating Scale. Results lead to concerns regarding the validity of some scores of the Pervasive Developmental Disorder Behavior Inventory.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.075
GPT teacher head0.297
Teacher spread0.222 · 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.

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

Citations4
Published2014
Admission routes1
Has abstractyes

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