Dissecting through barriers: interprofessional education, problem‐based learning, and gross anatomy (18.3)
Bibliographic record
Abstract
Healthcare delivery is reliant on a team‐based approach. Interprofessional education (IPE) provides a means by which such collaboration skills can be fostered. IPE within healthcare programs has been associated with many benefits, including improvements in patient care and satisfaction, reducing clinical error rates, and diminishing negative professional stereotypes. In recognition of the need for IPE, an IP gross anatomy dissection course was founded at McMaster University in 2009. Data has been collected from 5 cohorts to determine the influence of this IPE format on the attitudes and perceptions of students towards other health professions. Annually, 28 students from various programs are randomly assigned into IPE teams for 10 weeks. Sessions involve an anatomy and scope‐of‐practice presentation, a small‐group case‐based session, and a dissection. The Interdisciplinary Education Perception Scale (IEPS) and Readiness for Interprofessional Learning Scale (RIPLS) quantitatively measure pre‐ and post‐course attitudes and perceptions towards IPE and other health professions. Weekly surveys and culminating profession‐specific focus groups qualitatively evaluate these variables. 5 year pre‐ and post‐course IEPS and RIPLS scores show significant improvements in positive professional identity, competency and autonomy, role clarity and attitudes toward other health professions. Qualitative results corroborated these findings and made suggestions for the development of similar longitudinal IPE curricula. The implementation of a 10‐week IPE dissection course provides a unique and effective venue for learning about scope‐of‐practice, fostering positive professional identity, and fostering positive attitudes toward IPE and IP collaboration.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".