Interorganizational Relationships in British Columbia’s Community Overdose Response: Evaluating Community Partnerships as a Network Intervention
Bibliographic record
Abstract
Background: Surging overdose deaths in British Columbia (B.C.) prompted the development of Community Action Teams (CATs) - interorganizational networks that deliver overdose prevention strategies. The formation of CATs is a network intervention, aiming to optimize implementation by fostering connectivity. My research questions were: 1) How were the network intervention components (relational antecedents, structure, processes and outcomes) enacted in each of the participating CATs, and how (and why) were these similar or different?; 2) How did each of the CAT components influence strategic goals/collaborative objectives? Methods: This was a multiple case study using multiple methods. I adapted a framework for evaluating interorganizational relationships (IORs ) to understand partnership building relative to implementation goals/tasks. I selected 5 CATs as cases and used multiple sources of data to compile the case studies: social network survey; community survey; interviews; and document review. I conducted a social network analysis, a descriptive analysis of the community survey, a thematic analysis of interview transcripts, and a content analysis of key themes from documents. Findings were compiled into summary tables and narratively integrated. Results: Three cases were ultimately included. CAT 1 took on a ‘whole network’ approach in which strategic goals involved most CAT members and pertained to enhancing partnering. Structures, processes and outcomes in this CAT were more participatory and cohesive. CAT 2 funded small projects, which led to a few organizations participating in pockets of action. This may have led to lower network cohesiveness, higher centralization (compared to CAT 1), and relational challenges. CAT 3 funded small projects involving the participation of many CAT members, which fostered better relational processes compared to CAT 2. Formality and fairness were key convening processes. All three CATs were operating network-wide at a mid-range level of collaboration, and all met their implementation tasks but not necessarily their collaboration goals. Conclusion: Information about implementation tasks/goals can help us better understand network formation. Evaluating IORs as network interventions can lend insights about how and why certain networks are built in certain ways, and better enables us to study and provide guidance on these relationships in the future.
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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 | 0.035 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".