Role understanding and effective communication as core competencies for collaborative practice
Bibliographic record
Abstract
The ability to work with professionals from other disciplines to deliver collaborative, patient-centred care is considered a critical element of professional practice requiring a specific set of competencies. However, a generally accepted framework for collaborative competencies is missing, which makes consistent preparation of students and staff challenging. Some authors have argued that there is a lack of conceptual clarity of the "active ingredients" of collaboration relating to quality of care and patient outcomes, which may be at the root of the competencies issue. As part of a large Health Canada funded study focused on interprofessional education and collaborative practice, our goal was to understand the competencies for collaborative practice that are considered most relevant by health professionals working at the front line. Interview participants comprised 60 health care providers from various disciplines. Understanding and appreciating professional roles and responsibilities and communicating effectively emerged as the two perceived core competencies for patient-centred collaborative practice. For both competencies there is evidence of a link to positive patient and provider outcomes. We suggest that these two competencies should be the primary focus of student and staff education aimed at increasing collaborative practice skills.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".