Using an interprofessional competency framework to examine collaborative practice
Bibliographic record
Abstract
Healthcare organisations are starting to implement collaborative practice to increase the quality of patient care. However, operationalising and measuring progress towards collaborative practice has proven to be difficult. Various interprofessional competency frameworks have been developed that outline essential collaborative practice competencies for healthcare providers. If these competencies were enacted to their fullest, collaborative practice would be at its best. This article examines collaborative practice in six acute care units across Alberta using the Canadian Interprofessional Health Collaborative (CIHC) competency framework (CIHC, 2010 ). The framework entails the six competencies of patient-centred care, communication, role clarification, conflict resolution, team functioning and collaborative leadership (CIHC, 2010 ). We conducted a secondary analysis of interviews with 113 healthcare providers from different professions, which were conducted as part of a quality improvement study. We found positive examples of communication and patient-centred care supported by unit structures and processes (e.g. rapid rounds and collaborative plan of care). Some gaps in collaborative practice were found for role clarification and collaborative leadership. Conflict resolution and team functioning were not well operationalised on these units. Strategies are presented to enhance each competency domain in order to fully enact collaborative practice. Using the CIHC competency framework to examine collaborative practice was useful for identifying strength and areas needing improvement.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.008 |
| 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".