Performance-based competencies for culturally responsive interprofessional collaborative practice
Bibliographic record
Abstract
This paper will highlight how a literature review and stakeholder-expert feedback guided the creation of an interprofessional facilitator-collaborator competency tool, which was then used to design an interprofessional facilitator development program for the Partners for Interprofessional Cancer Education (PICE) Project. Cancer Care Nova Scotia (CCNS), one of the PICE Project partners, uses an Interprofessional Core Curriculum (ICC) to provide continuing education workshops to community-based practitioners, who as a portion of their practice, care for patients experiencing cancer. In order to deliver this curriculum, health professionals from a variety of disciplines required education that would enable them to become culturally sensitive interprofessional educators in promoting collaborative patient-centred practice. The Registered Nurses Professional Development Centre (RN-PDC), another PICE Project partner, has expertise in performance-based certification program design and utilizes a competency-based methodology in its education framework. This framework and methodology was used to develop the necessary interprofessional facilitator competencies that incorporate the knowledge, skills, and attitudes required for performance. Three main competency areas evolved, each with its own set of competencies, performance criteria and behavioural indicators.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".