Dissecting through the decade: a 10-year cross-sectional analysis of interprofessional experiences in the anatomy lab
Bibliographic record
Abstract
Interprofessional education (IPE) is prioritized as a critical component in preparing pre-licensure health professional students for effective teamwork and collaboration in the workplace to facilitate patient-centered care. Knowledge in anatomy is fundamental for healthcare professionals, making interprofessional anatomy education an attractive intervention for IPE and anatomy learning. Since 2009, the Education Program in Anatomy at McMaster University has offered an intensive 10-week IPE Anatomy Dissection elective to seven health professional programs annually. From 2011, students were invited to complete the Readiness for Interprofessional Scale (RIPLS) and Interprofessional Education Perception Scale (IEPS) before and after the elective. A total of 264 students from 2011 to 2020 completed RIPLS and IEPS. There were significant differences before and after the elective in students' total RIPLS scores and three of the four subscales: teamwork and collaboration, positive professional identity, and roles and responsibilities. Similarly, there were statistical differences in the total IEPS scores and two of three subscales: competency and autonomy and perceived actual cooperation. Statistically significant differences in RIPLS and IEPS total scores across several disciplines were also observed. This study demonstrates the elective's impact in improving students' IPE perceptions and attitudes, likely from the extended learning and exposure opportunity with other disciplines.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".