Community networks of services for pregnant and parenting women with problematic substance use
Bibliographic record
Abstract
Integrated treatment programs for pregnant and parenting women who use substances operate at the intersection of multiple service systems, including specialized substance use services, the broader health system, child protection, and social services. Our objectives were to describe the composition and structure of community care networks surrounding integrated treatment programs in selected communities in Ontario, Canada. We used a two-stage snowball method to collect network data from 5 purposively selected integrated treatment programs in communities in Ontario. Front-line staff with integrated treatment programs identified their top 5 service partners, who were then contacted and asked to provide the same information (n = 30). We used social network analysis to measure the cohesiveness, reciprocity, and betweenness centrality in the integrated treatment program's ego network. We described network composition in terms of representation of different service types. Across communities, common service partners were child protection, substance use or mental health services, parenting and child support, and other social services. Primary and pre-natal care, opioid agonist therapy, and legal services were rarely named as partners. Networks varied in network cohesiveness, as indicated by connectivity between the service partners and reciprocal ties to the integrated treatment programs. Integrated treatment programs commonly brokered the connections between other service partners. Findings suggest that these integrated treatment programs have achieved a level of success in developing cross-sectoral partnerships, with child protection services, parenting and child support, and social services featuring prominently in the networks. In contrast, there was a lack of close connections with physician-based services, highlighting a potential target for future quality improvement initiatives in this sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".