A realist review of family-based interventions for children of substance abusing parents
Bibliographic record
Abstract
BACKGROUND: Millions of children across North America and Europe live in families with alcohol or drug abusing parents. These children are at risk for a number of negative social, emotional and developmental outcomes, including an increased likelihood of developing a substance use disorder later in life. Family-based intervention programs for children with substance abusing parents can yield positive outcomes. This study is a realist review of evaluations of family-based interventions aimed at improving psychosocial outcomes for children of substance abusing parents (COSAPs). The primary objectives were to uncover patterns of contextual factors and mechanisms that generate program outcomes, and advance program theory in this field. METHODS: Realist review methodology was chosen as the most appropriate method of systematic review because it is a theory-driven approach that seeks to explore mechanisms underlying program effectiveness (or lack thereof). A systematic and comprehensive search of academic and grey literature uncovered 32 documents spanning 7 different intervention programs. Data was extracted from the included documents using abstraction templates designed to code for contexts, mechanisms and outcomes of each program. Two candidate program theories of family addiction were used to guide data analysis: the family disease model and the family prevention model. Data analysis was undertaken by a research team using an iterative process of comparison and checking with original documents to determine patterns within the data. RESULTS: Programs originating in both the family disease model and the family prevention model were uncovered, along with hybrid programs that successfully included components from each candidate program theory. Four demi-regularities were found to account for the effectiveness of programs included in this review: (1) opportunities for positive parent-child interactions, (2) supportive peer-to-peer relationships, (3) the power of knowledge, and (4) engaging hard to reach families using strategies that are responsive to socio-economic needs and matching services to client lived experience. CONCLUSIONS: This review yielded new findings that had not otherwise been explored in COSAP program research and are discussed in order to help expand program theory. Implications for practice and evaluation are further discussed.
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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 | 0.027 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".