Validity, Reliability, Accessibility, and Applicability of Young Children’s Developmental Screening and Assessment Tools across Different Demographics: A Realist Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".