Using Q‐methodology to determine students' perceptions of interprofessional anatomy education
Bibliographic record
Abstract
Interprofessional education (IPE) prepares healthcare students for collaboration in their future careers. The purpose of this study was to determine which aspects of the IPE Program in Anatomy at McMaster University contributed to the development of healthcare student's interprofessional skills. Q-methodology was used to identify the students' common viewpoints of the IPE experience. A total of 26/28 (93%) of students in the course from the medical, nursing, midwifery, physician assistant, occupational therapy, and physiotherapy programs participated in this study. Students were asked to sort a Q-sample of 43 statements about the IPE dissection course derived from previous qualitative studies of the program. Using the centroid factor extraction and varimax rotation, three salient factors (groups) emerged, namely: (1) Anatomy IPE Enthusiasts, (2) Practical IPE Advocates, and (3) Skeptical IPE Anatomists. The Anatomy IPE Enthusiasts believed that students from different disciplines brought unique anatomical knowledge and each group member guided others through difficult material. The Practical IPE Advocates expressed that they would be stronger advocates for interprofessional teams in the future because of the course. The Skeptical IPE Anatomists strongly disagreed that learning with students from different disciplines helped them gain an understanding of their roles in the context of other healthcare professionals and felt that there was little benefit from the IPE program compared to other non-interprofessional programs. These findings about student attitudes are critical to drive an evidence-based evolution of the IPE dissection course, since students' perceptions can have a profound influence on interprofessional collaboration in the workplace.
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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 | 0.034 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".