Professional Attitudes in Health Professions' Education: The Effects of an Anatomy Near‐Peer Learning Activity
Bibliographic record
Abstract
Interprofessional attitudes existing between healthcare disciplines can negatively impact communication and collaboration in the clinical setting. While human anatomy is a topic central to healthcare trainees, the potential of the anatomy laboratory to minimize negative interprofessional attitudes has yet to be characterized. This study aimed to assess the effects of an anatomy interprofessional near-peer learning activity (AIP-NPLA) on medical and nursing students' interprofessional attitudes at McGill University. The authors employed a convergent parallel mixed methods study to explore participants' AIP-NPLA experiences. The Attitudes to Health Professionals Questionnaire (AHPQ) was used pre- and post-AIP-NPLA to assess participants' attitudes toward their own and their counterpart profession. In addition, a focus group was held immediately following the AIP-NPLA to explore participants' experiences and interprofessional perceptions. Quantitative results using a principal components analysis demonstrated significant changes in nursing students' responses between pre- and post-AIP-NPLA scoring, rating the medical profession as being more caring overall. Medical students' responses pre- and post-AIP-NPLA demonstrated no significant differences. Qualitative results also suggested a breakdown of negative attitudes, an increased understanding of inter- and intra-professional roles, and the importance of interprofessional collaboration and mutual learning for their careers. These findings revealed that attitudes among healthcare trainees may be positively restructured in the anatomy laboratory, allowing for collaborative care to predominate in current and future clinical practices.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".