Student satisfaction and perceptions of small group process in case-based interprofessional learning
Bibliographic record
Abstract
BACKGROUND: The small group, case-based learning approach is believed to be a useful strategy for facilitating interprofessional learning and interaction factors are said to have a significant effect on student interest, learning and satisfaction with such approaches. AIM: The purpose of our study was twofold: assess students' satisfaction with a blended approach to interprofessional learning which combined computer-mediated and face-to-face, case-based learning; and examine the relationship between student satisfaction and perceptions of the collaborative learning process. METHOD: We introduced six interprofessional learning modules to approximately 520 undergraduate health professional students from medicine (61), nursing (351), pharmacy (20), and social work (89). All students were invited to complete an evaluation survey which assessed student satisfaction with the interprofessional learning experience and students' perceptions of the small group learning process. RESULTS: Students' satisfaction with interprofessional education was related to professional background. Students from across professions reported greater satisfaction with face-to-face, case-based learning when compared with other learning methods. A more positive perception of face-to-face, case-based learning was related to greater satisfaction with interprofessional learning. CONCLUSIONS: The findings support the case-based method in facilitating interprofessional learning and highlight the importance of effective facilitation of small-group collaborative learning to enhance student satisfaction with interprofessional learning experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".