Optimizing interprofessional education: Integrating a portfolio-based approach in undergraduate curriculum
Bibliographic record
Abstract
Background: Interprofessional education (IPE) is the need of the hour for any undergraduate curriculum to build interprofessional communication skills, adapt teamwork, and role clarity of various healthcare professionals in the early stage of a learning period. The outcome will improve patient safety and quality of care through a holistic approach by addressing lacunae in interprofessional collaboration. Objectives: To describe the steps to implement the IPE curriculum and evaluation of learners’ activity through an IPE portfolio. Results: Sensitization of IPE curriculum among various health professional faculties, and students. Develop core team facilitators, and construct lesson plans and assessments. The IPE portfolio will be used as a reliable and effective tool for formative assessment including authentic, real-world examples of learner’s work. Conclusion: This paper communicates undergraduates’ reflection practices in patient care stimulate critical thinking, deep and lifelong learning, and, facilitating from novice to mastery levels are achieved through integrated and aligned with IPE curriculum and portfolios-based assessment tool.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".