A Multiple Agencies and Counties Partnership: Improving Parental Substance Use and Services Delivery Outcomes through a Network Development And Collaboration
Bibliographic record
Abstract
Cross-sector collaboration has been defined as connecting or sharing information, resources, activities, and capabilities by organizations in two or more areas to achieve together an outcome that could not be achieved by organizations in one area separately (Bryson, Crosby, & Stone, 2006). To collaborate, agencies may need to consider questions, such as why to collaborate; what theory or theories will guide the collaboration; what are characteristics of effective networks; what governing structure will be used; how to evaluate the effectiveness of the collaboration network; and what are the benefits and challenges of collaboration. Because families are not being provided services within expected timeframes and because of a significant increase in foster care placements—which was largely driven by parental substance use—there is a need for collaboration among service providers in Central Virginia. A Multiple Agencies and Counties Partnership (MACP) was formed to address these problems. The purpose of this article is to present a case study of the development and accomplishments of MACP in Central Virginia and to relate the development and accomplishments of MACP to each of the aforementioned considerations, which may provide generalized lessons that other agencies may consider when collaborating. Keywords: collaboration, substance use, community, lessons learned
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".