TEAM-BASED LEARNING VS LECTURE-BASED LEARNING IN MEDICAL EDUCATION
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
Objective: The objective of the study is to determine better mode of learning for medical graduates by comparing team-based learning (TBL) and lecture-based learning methods. Study Design: Comparative analytical study. Place and Duration of Study: Surgical Ward 25 of Endocrine and General surgery, Jinnah Postgraduate Medical Center, Karachi, in April 2019. Methodology: This comparative study was based on the principles of TBL; the control program used the traditional lecture-based approach. Both programs were aimed at investigating the knowledge and performance of the two groups. Thirty surgical interns were included in this study. Two groups were made by random selection of surgical interns, 15 in TBL group and other 15 in traditional teaching group. TBL group (Group A) was given the topic of thyroid diseases for self-study followed by 1 h discussion amongst the group members. Lecture-based group (Group B) was given 1 h powerpoint presentation on similar topic. As the main outcome measures, questionnaire containing twenty best choice questions was given to both groups. Performance of the two groups was checked and results calculated as total, average, and standard deviation. Results: Group A participants’ total score (147) was higher than Group B (131) but the p-value was not found to be significant (0.144). Conclusion: Both forms of learning methods are effective and productive in medical education.
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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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