TEAM-BASED LEARNING VS LECTURE-BASED LEARNING IN MEDICAL EDUCATION
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.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".