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Record W4399858665 · doi:10.3390/children11060745

Validity, Reliability, Accessibility, and Applicability of Young Children’s Developmental Screening and Assessment Tools across Different Demographics: A Realist Review

2024· review· en· W4399858665 on OpenAlexaff
Stefan Kurbatfinski, Jelena Komanchuk, Aliyah Dosani, Nicole Létourneau

Bibliographic record

VenueChildren · 2024
Typereview
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsMount Royal UniversityOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsPsycINFOCINAHLCompendiumMEDLINEReliability (semiconductor)PsychologyDemographicsPsychometricsApplied psychologyClinical psychologyMedicinePsychological interventionPsychiatry

Abstract

fetched live from OpenAlex

Valid and reliable developmental screening and assessment tools allow professionals to identify disabilities/delays in children, enabling timely intervention to limit adverse lifelong impacts on health. However, differences in child development related to culture, genetics, and perinatal outcomes may impact tool applicability. This study evaluated the validity, reliability, and accessibility of multidomain developmental screening tools for young children, analyzed the applicability of tools across different contexts, and created a compendium of tools. Employing adapted realist review methods, we searched APA PsycInfo, MEDLINE, CINAHL, ERIC, and Google to identify relevant articles and information. We assessed accessibility, validity, reliability, and contextual applicability (N = 4110 evidence sources) to create tool ratings and make recommendations. Of 33 identified tools, 22 were screening and 11 were assessment tools. Fewer screening tools than assessment tools were rated highly overall. Evidence for use in different cultures was often lacking for both types of tools. The ASQ (screening) and BDI (assessment) tools were rated most favorably and are recommended for use, though other tools may be more applicable in different contexts (e.g., NEPSY among children with Asperger's Syndrome). Future research should focus on assessing the validity and reliability of tools across different demographics to increase accessibility and ensure all children are properly supported.

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)0.005
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.542
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0010.002
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 head0.150
GPT teacher head0.465
Teacher spread0.315 · 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
GenreReview

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

Citations6
Published2024
Admission routes1
Has abstractyes

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