Integrating an Interprofessional Education Model at a Private University
Bibliographic record
Abstract
Objective: Eight cohorts of 25 nursing, optometry, pharmacy, physical therapy, and health care administration students will be “collaborative practice-ready” using care coordination principles for primary, secondary, and tertiary prevention. Methods: In each academic semester, 5 students from each of the health professional programs for a total of 25 students per cohort were asked to participate. The two semester interprofessional activity used online learning via Blackboard Learn ® , face-to-face and simulated experiences to teach the core competencies of Interprofessional education and collaborative practice (IPECP). In the first semester core, students were equipped with knowledge specific to teamwork and specific to care of vulnerable populations with diabetes. The organizing framework for the curriculum sequence included concepts of health promotion, prevention and intervention embedded in the Web of Causation (Primary, Secondary, Tertiary care). Specific content on diabetes care including standards of care will built on specialty content embedded in each health professional curriculum. Results: Over 225 students participated in the interprofessional educational activity. Several of the disciplines used multiple methods to invite students to participate in this voluntary effort while one discipline provided a means by which students could select this activity as an elective to meet their graduation requirements and another integrated the activity into an existing course. Conclusions: While not completely new, transforming the way students and faculty in health professions education experience clinical and didactic environments is an imperative. Interprofessional education and clinical practice must be an intentional practice that needs and requires support from university administration for successful implementation and sustainability.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".