Case Based Learning at an Indian Medical College
Bibliographic record
Abstract
Background: Current literature promotes a student-centred approach with an active learning design, as such curricula have demonstrated improvements in outcomes such as critical thinking and clinical competence. Current literature demonstrates success in North America and Europe. However, research in low-resource countries have highlighted resource- and satisfaction-related issues due to such shifts. This study implements a case-based learning (CBL) intervention at Kasturba Medical College Mangalore Campus (KMCMG), India. Faculty and undergraduate medical student perceptions are explored in order to understand the contextual factors that will lead to an effective, acceptable and feasible medical curriculum. Methods: This cross-sectional, mixed-methods study employed a Likert scale questionnaire and semi-structured focus groups to 3rd year medical students (n=248), as well as semi-structured interviews with faculty (n=10) in the Department of Community Medicine. Cases were created through a co-development process with KMCMG faculty. Questionnaire data was analyzed by descriptive statistics and qualitative data was analyzed primarily by an inductive-iterative approach. Results: Both faculty and students find CBL to be more valuable than the traditional lecture-based method, and find CBL meaningful for students as future physicians. Comments highlighted the importance of student preparedness and of trained facilitators in order to enhance the learning experience. A significantly larger proportion of Indian schooled students, versus those who studied abroad, felt that CBL helped acquire new information (p=0.016), enhanced their clinical approach (p=0.008), and believed the role of the facilitator was important (p=0.001). Conclusion: Feedback towards CBL was found to be satisfactory in all aspects, and both students and faculty would like to see more CBL sessions in the future. Limitations such as faculty shortage and the inability to use informational technology at this time should be taken into consideration when moving forward. It is recommended that a resource-light version of CBL be considered, to provide robust orientations to faculty and students, and to further engage with faculty and students in order to enhance the CBL experience.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".