Development of a Longitudinal Curricular Evaluation Framework for Intra- and Interprofessional Teamwork
Bibliographic record
Abstract
Objectives: To describe the development and evaluation of a university-wide competency and evaluation framework for intra- and interprofessional education (IPE) teamwork.Methods: Development of the framework was based on existing literature and specific contexts of the schools within our university. Evaluation and program alignment regarding use of the framework were achieved through qualitative interviews with deans of the Schools of Medicine, Nursing, and Pharmacy, and focused on how they evaluated student progression towards the university-wide teamwork competency. Interview data were analyzed using classical content analysis.Results: Despite efforts to carefully design the framework, interviews revealed that significant variation exists regarding when and how both IPE and team-based care are taught and evaluated across schools. Common barriers to interprofessional education included variations in teamwork practices across disciplines, scheduling challenges, and lack of resources for implementation. Recommendations for how to align teaching and evaluation activities with the framework are posed.Conclusions: Longitudinally tracking the development of interprofessional competencies within/across health professions schools requires careful planning and collaboration among institutional leaders, interprofessional educators, program evaluators, and students. The information gained from this process provides insights toward implementing future high-quality IPE in teamwork and other inter- and intraprofessional competencies, which may be helpful to others.
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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 | 0.149 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".