Flipping the Script: A Design thinking Approach to Enhancing Interprofessional Collaboration in Primary Care
Bibliographic record
Abstract
Context: The literature underscores how time-consuming and difficult it is to learn to collaborate. The challenges faced stem from the uncertainty and complexity of redefining professional roles and scopes of practice, as well as the skills needed to effectively work together and coordinate patient care as a cohesive team. Understanding each team member9s role, fostering open communication channels, and establishing clear administrative procedures are highlighted as essential elements for fostering successful collaborative practices within the clinic. Objective: To enhance interprofessional collaboration among primary care teams within family physician groups (FMGs) and to optimize care resources along clear and simple care trajectories. Study design: Design thinking process. We developed an approach to assist healthcare professionals in developing a tool aimed to informing about each team member areas of expertise and optimal care trajectories. Setting: One multidisciplinary primary care team in the province of Quebec, Canada. Population studied: Multidisciplinary team including 10 family physicians, 3 nurses, 1 pharmacist, 1 social worker, 1 physiotherapist and 3 administrative staff. Intervention/instrument: The research team worked for about 6 months with the primary care team to enhance interprofessional collaboration. One intervention consisted in facilitating discussions to highlight the role of each healthcare professional. Then, the team embarked into a design thinking approach to develop and refine an interprofessional collaboration tabletop flip menu. Results: The tabletop flip menu developed is divided into two sections. The first section lists one health condition per page and explains how and when each health professional can be involved, and the referral process. The second section focuses on the expertise of each health professionals to whom a family physician may make a referral, but where there is no more than one provider to address this health condition. This tabletop flip menu is available in each exam room and to all administrative staff at the clinic. Conclusion: This collaborative process and tabletop menu can promote interprofessional communication and team cohesion. It also seems promising to optimize collaboration as well as the scope of practice for all professionals involved in primary care team.
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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 | 0.046 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".