Exploring Intra and Interorganizational Integration Efforts Involving the Primary Care Sector – A Case Study from Ontario
Bibliographic record
Abstract
Background: organizational integration between primary care and other community partners involved in caring for complex patients. Methods: Two care coordination initiatives (Health Links) were selected - one led by a primary care team with a high level of intraorganizational integration as assessed by the Collaborative Practice Assessment Tool (CPAT), and the other led by a primary care team with a low level of intraorganizational integration. A case study design involving a social network approach was used to assess interorganizational integration across six types of relationships including regular contact, perceived level of integration, referrals, information sharing, joint care planning, and shared resources. Results: Compared to the high-CPAT led case, the low-CPAT led case had higher density (more ties among organizations) in terms of regular contact, integration, and sharing of resources, whereas the opposite was true for the referral, information sharing, and joint care planning networks. Network centralization (extent to which network activity is influenced by one or a group of organizations) was higher for the high-CPAT case compared to the low-CPAT case in the integration, referrals, and joint care planning networks, while the low-CPAT case had higher centralization with regard to regular contact, information sharing, and shared resources. Conclusion: The interplay between intra and interorganizational integration remains unclear. We found no consistent differences in the patterns of ties across the six types of networks examined between the two cases. Assessing changes in network metrics for different organizations in each case over time, and supplementing network findings through in-depth interviews with network members are key next steps to consider.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".