Communication and Interprofessional Collaboration in Primary Care: From Ideal to Reality in Practice
Bibliographic record
Abstract
To improve patient-centered care, many health care systems are mandating interprofessional collaboration (IPC). However, in many primary care contexts, IPC is still nascent and fraught with tension. Communication is thought to be a key determinant of IPC, but few studies empirically examine IP communication practices. Therefore, we report here on the qualitative portion of a mixed methods pilot study investigating observed IPC and communication in primary care clinics in Quebec, Canada. Studying actual communication practices to understand collaborative activities, we seek to investigate how the ideals of patient centeredness and clinical democracy put forward in the IP literature stack up against actual IPC practice in primary care. Qualitative data was gathered by shadowing health professionals in two primary care clinics, and analyzed through thematic coding. A typology of observed IP practices was created and compared to the continuum of interprofessional collaborative practice. Further analysis focused on how participants made sense of their collaboration, especially why, how and with whom they collaborated. Findings were grouped into three categories of communicative actions: coordinating sequential efforts; assisting others' sensemaking; and working to understand together. Implications for practice and future research are discussed.
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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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.051 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".