Dissecting through barriers: A mixed‐methods study on the effect of interprofessional education in a dissection course with healthcare professional students
Bibliographic record
Abstract
Healthcare delivery is reliant on a team-based approach, and interprofessional education (IPE) provides a means by which such collaboration skills can be fostered prior to entering the workplace. IPE within healthcare programs has been associated with improved collaborative behavior, patient care and satisfaction, reduced clinical error, and diminished negative professional stereotypes. An intensive interprofessional gross anatomy dissection course was created in 2009 to facilitate IPE at McMaster University. Data were collected from five cohorts over five years to determine the influence of this IPE format on the attitudes and perceptions of students towards other health professions. Each year, 28 students from the medicine, midwifery, nursing, physician's assistant, physiotherapy, and occupational therapy programs were randomly assigned into interprofessional teams for 10 weeks. Sessions involved an anatomy and scope-of-practice presentation, a small-group case-based session, and a dissection. A before/after design measured changes in attitudes and perceptions, while focus group data elaborated on the student experience with the course. Pre- and postmatched data revealed significant improvements in positive professional identity, competency and autonomy, role clarity and attitudes toward other health professions. Qualitative analysis of intraprofessional focus group interviews revealed meaningful improvements in a number of areas including learning anatomy, role clarity, and attitudes towards other health professions.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".