METHOD/MODEL PRESENTATION Competencies for Interprofessional Collaboration
Bibliographic record
Abstract
collaboration in health care is now considered a high priority, as concerns about patient safety, health and human resources shortages, and effective and efficient care have reached epic proportions. Although there are many models for interprofessional education for collaborative, patient-centered care, there is little in the literature to describe competencies for an interprofessional collaborative practitioner. This article will describe an emerging Canadian competency framework for interprofessional collaboration that (l) considers previous descriptions of collaborative practice and (2) uses existing literature to support a model for describing competencies for collaborative practice. Model Description and Evaluation. In this emerging competency framework, 6 competency domains are described using a competency statement and a set of associated descriptors. The collaborative leadership, dealing with interprofessional conflict, team functioning, and role clarification domains intersect with all of the others, yet are distinct and require focused descriptions. While patient-centered care Lesley Bainbridge is associate principal of the
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".