Implementation of interprofessional team-based care: A cross-case analysis
Bibliographic record
Abstract
Two out of five Canadians have at least one chronic disease and four out of five are at risk of developing a chronic disease. Successful disease management relies on interprofessional team-based approaches, yet lack of purposeful cultivation and patient engagement has led to systematic inefficiencies. Two primary care teams in Southwestern Ontario implementing interprofessional chronic care programs for patients with chronic obstructive pulmonary disease were compared. A mixed-methods cross-case analysis was conducted including interviews, focus groups, observations and document analysis. Cases (n = 2) were chosen based on intrinsic and unique value. Participants (n = 46) were sampled using a combination of purposive and multi-level sampling. Data was analyzed using an iterative process; inductive coding was used to gain a sense of context followed by a deductive cross-case analysis to compare and contrast themes across sites. Kompier's five-step framework was used to assess factors contributing to successful implementation and to provide insight into interactions between teams, providers and patients. Both cases satisfied all five factors (systemic and gradual approach, identification of risk factors, theory-driven, participatory approach and sustained committed support). However, one case was more successful at fully implementing their model, attributed to a flexible implementation, plans to mitigate risks, theory use, a supportive team and continued buy-in from leadership. By better understanding key facilitators and barriers, we can support the implementation of chronic disease management programs, foster sustainability of high-performing interprofessional teams, and engage patients in the development and maintenance of team-based chronic disease management.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".