Psychometrics of psychosocial behavior items under age 6 years: Evidence from Nebraska, USA
Bibliographic record
Abstract
Because healthy psychosocial development in the first years of life is critical to lifelong well-being, governmental, and nongovernmental organizations are increasingly interested in monitoring psychosocial behaviors among populations of children. In response, the World Health Organization is developing the Global Scales of Early Development Psychosocial Form (GSED PF) to facilitate population-level psychosocial monitoring. Once validated, the GSED PF will be an open-access, caregiver-reported measure of children's psychosocial behaviors that is appropriate for infants and young children. This study examines the psychometric validity evidence from 45 items under consideration for inclusion in the GSED PF. Using data from N = 836 Nebraskan (USA) children aged 180 days to 71 months, results indicate that scores from 44 of the 45 (98%) items exhibit positive evidence of validity and reliability. A bifactor model with one general factor and five specific factors best fit the data, exhibited strong reliability, and acceptable model fit. Criterion associations with known predictors of children's psychosocial behaviors were in the expected direction. These findings suggest that measurement of children's psychosocial behaviors may be feasible, at least in the United States. Data from more culturally and linguistically diverse settings is needed to assess these items for global monitoring.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".