Case Based Learning Teaching Methodology in Undergraduate Health Sciences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Case-based learning (CBL) is an interactive teaching approach involving small-group discussion to determine a range of solutions for a presented patient case. In light of the success that the approach has achieved in numerous professional and undergraduate programs, a pilot project was introduced in 2009 by senior health sciences students, who acted as CBL facilitators, at the University of Ottawa for undergraduate courses in the Interdisciplinary School of Health Sciences (ISHS). In collaboration with faculty professors, the facilitators developed CBL sessions consisting of patient cases that were reflective of the core objectives of health sciences courses. A total of 144 undergraduate students from three ISHS courses took part in these sessions; they were evaluated based on the calibre of their participation and a quiz. The quiz consisted of 5 questions that evaluated the students’ mastery of the concepts covered in the CBL session. The students also completed an evaluation of the pilot project. On a nominal scale of one to five, the students on average scored 4.13 out of a possible 5.00 (SD 1.48) marks on the quiz. In the evaluation, the students rated the project as having an overall learning benefit of 3.82 on a nominal scale of one to four. The evaluation indicates that the students perceived the program as having significant learning value and the quiz marks confirmed that CBL promoted the application of lecture content to practical scenarios. These preliminary findings suggest that implementing CBL in ISHS would enhance students’ academic experience. Further sessions based on this model would improve from more rigorous pre- and post- session assessments.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.147 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it