Family physicians collaborating for health system integration: a scoping review
Bibliographic record
Abstract
Abstract Background In Canada, Ontario Health Teams (OHTs) are a new model for integrated healthcare. Core to OHTs are family physicians (FPs) and their ability to collaborate with other FPs and healthcare providers. Whereas the factors for intra-organizational collaboration have been well-studied, inter-organizational collaboration between FPs and other healthcare organizations as an integrated care network, are less understood. This paper aims to explore the structural factors, processes, and theoretical frameworks that support FPs’ collaboration for integrated healthcare. Methods A scoping review was undertaken based on Joanna Briggs Institute (JBI) methodology for scoping review and using the Preferred Reporting Items for Systematic Review and Meta-Analysis for Scoping Review (PRISMA_ScR) checklist. A search for academic and relevant grey literature published between 2000–2021 was conducted across databases (MEDLINE, EMBASE, EBSCOhost).Thematic analysis was used to identify the key findings of the selected studies. Results Thirty-two studies were included as eligible for this review. Three structural components were identified as critical to FPs’ successful participation in inter-organizational partnerships: (1) shared vision/values, (2) leadership by FPs, and (3) defined decision-making procedures. Also, three processes were identified: (1) effective communication, (2) a collective sense of motivation for change, and (3) relationships built on trust. Three theoretical frameworks provided insight into collaborative initiatives: (1) Social Identity Approach, (2) framework of interprofessional collaboration, and (3) competing values framework. Conclusion FPs hold unique positions in healthcare and this review is the first to synthesize the best evidence for building collaborations between FPs and other healthcare sectors. These findings will inform collaboration strategies for healthcare integration, including with OHTs.
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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 | 0.045 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".