A Multicomponent Early Intervention Program and Trajectories of Behavior, Cognition, and Health
Bibliographic record
Abstract
OBJECTIVES: To investigate the developmental impact of a prenatal–to–age-5 multicomponent early intervention program targeting families living in low socioeconomic conditions. METHODS: Pregnant women from a disadvantaged Irish community were randomly assigned into a treatment group (home visits, baby massage, and parenting program; n = 115) or control group (n = 118). Children’s behavioral problems (externalizing, internalizing), cognitive skills (general, vocabulary), and health service use (number of health clinic visits), were regularly assessed (6 months to 4 years of age). Children’s developmental trajectories were modeled by using latent class growth analyses to test whether certain subgroups benefited more than others. RESULTS: High and low developmental trajectories were identified for each outcome. Treated children were more likely to follow the high-level trajectory for cognition (odds ratio = 2.89; 95% confidence interval = 1.55–5.50) and vocabulary skills (odds ratio = 2.02; 95% confidence interval = 1.08–3.82). There were no differences by treatment condition in the risk of belonging to a high externalizing or high health clinic visit trajectory. However, within the high externalizing trajectory, treated children had lower scores than controls (Hedges’ g range (2–4 years) = 0.45–0.58; P < .05) and, within the high health clinic visit trajectory, only children in the control group experienced an increasing number of visits. CONCLUSIONS: This program revealed moderate positive impacts on trajectories of cognitive development and number of health clinic visits for all children, whereas positive impacts on externalizing behavior problems were restricted to children with the most severe problems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".