Maternal well-being and its association to risk of developmental problems in children at school entry
Bibliographic record
Abstract
BACKGROUND: Children at highest risk of developmental problems benefit from early identification and intervention. Investigating factors affecting child development at the time of transition to school may reveal opportunities to tailor early intervention programs for the greatest effectiveness, social benefit and economic gain. The primary objective of this study was to identify child and maternal factors associated with children who screened at risk of developmental problems at school entry. METHODS: An existing cohort of 791 mothers who had been followed since early pregnancy was mailed a questionnaire when the children were aged four to six years. The questionnaire included a screening tool for developmental problems, an assessment of the child's social competence, health care utilization and referrals, and maternal factors, including physical health, mental health, social support, parenting morale and sense of competence, and parenting support/resources. RESULTS: Of the 491 mothers (62%) who responded, 15% had children who were screened at high risk of developmental problems. Based on a logistic regression model, independent predictors of screening at high risk for developmental problems at age 5 were male gender (OR: 2.3; 95% CI: 1.3, 4.1), maternal history of abuse at pregnancy (OR: 2.4; 95% CI: 1.3, 4.4), and poor parenting morale when the child was 3 years old (OR: 3.9; 95% CI: 2.1, 7.3). A child with all of these risk factors had a 35% predicted probability of screening at high risk of developmental problems, which was reduced to 13% if maternal factors were favourable. CONCLUSIONS: Risk factors for developmental problems at school entry are related to maternal well being and history of abuse, which can be identified in the prenatal period or when children are preschool age.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".