Promoting interprofessional learning with medical students in home care settings
Bibliographic record
Abstract
BACKGROUND: The home care setting is ideal for medical students to learn about the importance of interprofessional collaboration in the community. AIMS: This project examined the impact of a unique program designed to facilitate medical students' knowledge and awareness of the challenges of interprofessional care in the home. METHODS: In pairs, medical students participated in two community visits with preceptors from different professions. Students completed a structured personal reflection after their first visit. Students and preceptors participated in focus groups or interviews to identify strengths and challenges of the experiences. The structured reflections and the focus group and interview transcripts were analyzed qualitatively. RESULTS: 164 medical students and 36 preceptors participated in 326 visits. There were high ratings of satisfaction from students and preceptors. Students developed unexpected insights into peoples' lives, developed a greater understanding of the patient's perspective and determinants of health, learned about others' scope of practice, and developed an appreciation of the limitations of their own scope of practice. Preceptors had high expectations for student performance and engagement and enjoyed the opportunity to impart their knowledge to future physicians. CONCLUSION: Although organizationally complex, the program evaluation suggestions that students and preceptors benefit from interprofessional experiences in the home.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".