A clinical nursing rotation transforms medical students’ interprofessional attitudes
Bibliographic record
Abstract
This study explores the extent to which a one-week nursing rotation for medical students changed the interprofessional attitudes of the participating nurses and students. Third-year medical students worked with nurses before starting clinical rotations. Pre- and post-experience surveys assessing perceptions of mutual respect, nurse-doctor roles, and interprofessional communication and teamwork were given to 55 nurses and 57 students. The surveys consisted of qualitative questions and a Likert scale questionnaire that was analyzed using qualitative and quantitative content analyses. The response rate was 51/57 (89%) students and 44/55 (80%) nurse preceptors. Nurses reported that students met nurses' expectations by displaying responsibility, respect, effective communication, and an understanding of nursing roles. Medical students' narratives demonstrated two significant changes. First, their views of nurses changed from that of physician helpers to that of collaborative patient-centred professionals. Second, they began defining nursing not by its tasks, but as a caring- and communication-centred profession. Responses to Likert-scaled questions showed significant differences corresponding to changes described in the narrative. A one-week immersive clinical nursing rotation for medical students was a transformative way of learning interprofessional competencies. Learning in an authentic workplace during a clinical rotation engendered mutual respect between nurses and future doctors. Students' view of the role of nurses changed from nurses working for doctors with patients, to working with doctors for patients.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".