Risk Markers for Poor Developmental Attainment in Young Children
Bibliographic record
Abstract
OBJECTIVE: To evaluate social and environmental determinants of poor developmental attainment among preschool children by means of longitudinal data from a population-based sample of Canadian children. DESIGN: Secondary analysis of data from cycles 1 (1994-1995) and 2 (1996-1997) of the National Longitudinal Survey of Children and Youth using a cohort design with 2-year follow-up. PARTICIPANTS: A total of 4987 children aged 1 to 5 years at baseline, whose biological mother completed risk factor information and who were included in both cycles. MAIN OUTCOME MEASURES: Poor developmental attainment (developing unusually slowly) was defined as scores more than 1 SD below the age-standardized mean for the Motor and Social Development Scale, revised Peabody Picture Vocabulary Test, or Canadian Achievement Tests in mathematics and reading/comprehension, depending on the child's age. RESULTS: The prevalence of sustained poor developmental attainment after 2 years of follow-up was 4.6%. Factors found to be associated with poor developmental attainment included male sex (odds ratio [OR], 1.37; 95% confidence interval [CI], 1.10-1.70), maternal depression (OR, 1.64; 95% CI, 1.25-2.15), low maternal education (OR, 1.57; 95% CI, 1.19-2.08), maternal immigrant status (OR, 1.93; 95% CI, 1.38-2.71), and household low income adequacy (OR, 1.43; 95% CI, 1.11-1.83). CONCLUSIONS: Having a mother who has symptoms of depression, has low education, or is an immigrant, and living in a household with low income adequacy increase the risk of poor developmental attainment in children aged 1 to 5 years. The notable risks associated with these factors indicate them as possible targets for screening and interventions to prevent poor developmental attainment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".