Effects of integrated programs for substance‐involved mothers on infant and child development outcomes: A systematic review and meta‐analysis
Bibliographic record
Abstract
Maternal substance use is a pressing public health issue that confers risk for maternal health, the parent-infant relationship, and child development. Integrated interventions that jointly address maternal substance use and child development have shown promise for enhancing child outcomes. No research to date has focused exclusively on the outcomes of young children or examined potential moderators of the effect sizes of integrated programs. This review evaluates the pooled effect of integrated interventions for substance-involved mothers on the developmental outcomes of their children. A comprehensive search strategy was conducted in seven databases (APA PsycINFO, CINAHL, Cochrane CENTRAL, Embase, MEDLINE, Sociological Abstracts, Web of Science) from January 2011 and May 2023. Studies were included if they reported on an intervention with at least one substance use treatment and one parenting or child treatment service for substance-involved mothers of children under 6 years of age. A total of 21 studies met inclusion criteria, and 14 nonoverlapping studies reported on effect sizes with a pooled effect size of SMD = .470 (95% CI = .35, .59). There was a trend toward treatment duration being a significant moderator (p = .08). Additional high-quality studies are needed to demonstrate the long-term impact of these interventions.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.023 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".