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Record W4315650942 · doi:10.3389/fped.2022.1080163

The influence of sociodemographic factors and response style on caregiver report of infant developmental status

2023· article· en· W4315650942 on OpenAlexaff
Amy K. Connery, Radhika S. Raghunathan, Alison M. Colbert, László A. Erdődi, Seth Warschausky, Alissa Huth‐Bocks, H. Gerry Taylor, Trivellore E. Raghunathan, Patricia A. Berglund, Angela D. Staples, Angela Lukomski, Jazmine Kirkland, Jennifer Cano, Renée Lajiness-O’Neill

Bibliographic record

VenueFrontiers in Pediatrics · 2023
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsUniversity of Windsor
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of Health
KeywordsMedicineDevelopmental psychologyEarly childhoodTemperamentStyle (visual arts)PsychologyClinical psychologyPersonalitySocial psychology

Abstract

fetched live from OpenAlex

Caregiver report is the most feasible way to assess early childhood development but is susceptible to the influences of response style and sociodemographic factors. In a sample of 571 caregiver-infant dyads (47.8% female; 48% White), we compared caregiver reports on the Ages and Stages Questionnaire-Third Edition (ASQ-3) with reports on a novel, web-based assessment, PediaTrac™. Ratings on PediaTrac correlated with ratings on the ASQ-3 at all time points (2, 4, 6, and 9 months). Caregiver age, response style, and sociodemographic factors accounted for significant variance on both measures. Developmental reporting of early childhood skills is influenced by caregiver response style and sociodemographic factors. These influences must be considered in order to ensure the accurate identification of infant developmental status.

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)0.020
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 score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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

Citations14
Published2023
Admission routes1
Has abstractyes

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