Comparison of Learning Outcomes Among Medical Students in Thailand to Determine the Right Time to Teach Forensic Medicine: Retrospective Study
Bibliographic record
Abstract
Background: Forensic medicine requires background medical knowledge and the ability to apply it to legal cases. Medical students have different levels of medical knowledge and are therefore likely to perform differently when learning forensic medicine. However, different medical curricula in Thailand deliver forensic medicine courses at different stages of medical study; most curricula deliver these courses in the clinical years, while others offer them in the preclinical years. This raises questions about the differences in learning effectiveness. Objective: We aimed to compare the learning outcomes of medical students in curricula that either teach forensic medicine at the clinical level or teach it at the preclinical level. Methods: This was a 5-year retrospective study that compared multiple-choice question (MCQ) scores in a forensic medicine course for fifth- and third-year medical students. The fifth-year students' program was different from that of the third-year students, but both programs were offered by Mahidol University. The students were taught forensic medicine by the same instructors, used similar content, and were evaluated via examinations of similar difficulty. Of the 1063 medical students included in this study, 782 were fifth-year clinical students, and 281 were third-year preclinical students. Results: The average scores of the fifth- and third-year medical students were 76.09% (SD 6.75%) and 62.94% (SD 8.33%), respectively. The difference was statistically significant (Kruskal-Wallis test: P<.001). Additionally, the average score of fifth-year medical students was significantly higher than that of third-year students in every academic year (all P values were <.001). Conclusions: Teaching forensic medicine during the preclinical years may be too early, and preclinical students may not understand the clinical content sufficiently. Attention should be paid to ensuring that students have the adequate clinical background before teaching subjects that require clinical applications, especially in forensic medicine.
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How this classification was reachedexpand
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".