Facilitating Implementation of Interprofessional Collaborative Practices into Primary Care: A Trilogy of Driving Forces
Bibliographic record
Abstract
Implementing interprofessional collaborative practices in primary care is challenging, and research about its facilitating factors remains scarce. The goal of this participatory action research study was to better understand the driving forces during the early stage of the implementation process of a community-driven and patient-focused program in primary care titled "TRANSforming InTerprofessional cardiovascular disease prevention in primary care" (TRANSIT). Eight primary care clinics in Quebec, Canada, agreed to participate by creating and implementing an interprofessional facilitation team (IFT). Sixty-three participants volunteered to be part of an IFT, and 759 patients agreed to participate. We randomized six clinics into a supported facilitation ("supported") group, with an external facilitator (EF) and financial incentives for participants. We assigned two clinics to an unsupported facilitation ("unsupported") group, with no EF or financial incentives. After 3 months, we held one interview for the two EFs. After 6 months, we held eight focus groups with IFT members and another interview with each EF. The analyses revealed three key forces: (1) opportunity for dialogue through the IFT, (2) active role of the EF, and (3) change implementation budgets. Decision-makers designing implementation plans for interprofessional programs should ensure that these driving forces are activated. Further research should examine how these forces affect interprofessional practices and patient outcomes.
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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 | 0.057 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".