Validation of the short version of the dimensional inventory for child development assessment
Bibliographic record
Abstract
OBJECTIVES: There is a critical need to monitor the development of children around the world, and in Brazil, this need is substantial since there is a paucity of assessment tools. This study aimed to describe the design and provide evidence of reliability and validity for the short version of the Dimensional Inventory for Child Development Assessment (IDADI-short). METHODS: A sample of 1,865 biological mothers of children aged 4-72 months (M = 34.8, SD = 20.20) completed the IDADI to assess Cognitive, socio-emotional, Expressive, and Receptive Language and Communication, Fine and Gross Motor, and Adaptive Behavior development. The psychometric properties of a total of 118 subscales of IDADI were obtained and the IDADI-short age-specific scores were correlated with the original inventory, and criteria variables such as neurodevelopment diagnosis, socioeconomic status, and sex. RESULTS: Item Response Theory analysis, Cronbach's Alpha, and McDonald's Omega indicated excellent internal consistency and optimal participant discrimination after minor alterations. IDADI-short scores were strongly associated with the original inventory, with high sensibility and specificity precision for developmental delays. Significant associations with relevant criteria variables were also observed. CONCLUSION: Findings support the use of IDADI-short as a parental measure of young children's development.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".