Maternal substance use and integrated treatment programs for women with substance abuse issues and their children: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: The rate of women with substance abuse issues is increasing. Women present with a unique constellation of risk factors and presenting needs, which may include specific needs in their role as mothers. Numerous integrated programs (those with substance use treatment and pregnancy, parenting, or child services) have been developed to specifically meet the needs of pregnant and parenting women with substance abuse issues. This synthesis and meta-analysis reviews research in this important and growing area of treatment. METHODS: We searched PsycINFO, MedLine, PubMed, Web of Science, EMBASE, Proquest Dissertations, Sociological Abstracts, and CINAHL and compiled a database of 21 studies (2 randomized trials, 9 quasi-experimental studies, 10 cohort studies) of integrated programs published between 1990 and 2007 with outcome data on maternal substance use. Data were summarized and where possible, meta-analyses were performed, using standardized mean differences (d) effect size estimates. RESULTS: In the two studies comparing integrated programs to no treatment, effect sizes for urine toxicology and percent using substances significantly favored integrated programs and ranged from 0.18 to 1.41. Studies examining changes in maternal substance use from beginning to end of treatment were statistically significant and medium sized. More specifically, in the five studies measuring severity of drug and alcohol use, the average effect sizes were 0.64 and 0.40, respectively. In the four cohort studies of days of use, the average effect size was 0.52. Of studies comparing integrated to non-integrated programs, four studies assessed urine toxicology and two assessed self-reported abstinence. Overall effect sizes for each measure were not statistically significant (d = -0.09 and 0.22, respectively). CONCLUSIONS: Findings suggest that integrated programs are effective in reducing maternal substance use. However, integrated programs were not significantly more effective than non-integrated programs. Policy implications are discussed with specific attention to the need for funding of high quality randomized control trials and improved reporting practices.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.069 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".