Correlation of seminar attendance and written examinations in medical education
Bibliographic record
Abstract
Objectives: The parts of constructive alignment, i.e. learning objectives, activities and assessment are crucial for good learning outcomes. However, they must constantly be evaluated so as to verify the alignment. Our aim was to investigate if attendance to our casebased seminars in family medicine contributed to exam performance and whether gender had any impact for undergraduate students at the medical school of Lund University in Sweden.Material and methods: Student performances in assessments of eleven consecutive classes (semesters) were studied and the attendance rate was documented as well as gender. These data were then used to analyse the correlation with the results on the written exam with linear regression and multilevel linear regression. Attendance was optional.Results: The marks on the written exam rose by 0.70 points (95% CI 0.49-0.90) corresponding with every seminar attended, 0.61 (95% CI 0.39-0.84) for men, 0.79 (95% CI 0.55-1.03) for women. Maximum points were 40. There was no detectable influence of teachers.Conclusions: For the majority of medical students, it is worthwhile to attend case-based seminars in family medicine as much as possible to enhance results in written exams. However, a few can skip seminars altogether and still pass their exams.
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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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".