Medical School Radiology Lectures: What Are Determinants of Lecture Satisfaction?
Bibliographic record
Abstract
OBJECTIVE: Slideshow presentations are a popular teaching method in undergraduate medical education; however, there are scant data on determinants of lecture satisfaction. The purpose of this study is to determine which features of undergraduate medical school radiology lectures are associated with better student evaluations. MATERIALS AND METHODS: All undergraduate medical school radiology presentations and student evaluations at the University of Ottawa from January to December 2013 were compiled. A standardized data extraction sheet was applied by two independent reviewers, including a 10% overlap for audit. Student evaluations were reported on a 5-point Likert scale from which an overall mean score per lecture was calculated. Correlation coefficients were calculated for continuous variables in relation to mean evaluation score. Student t tests and univariate ANOVAs were performed for categoric data. Quantitative content analysis of student comments was also undertaken. RESULTS: Sixty-four slideshows by 33 lecturers were analyzed. The overall mean (SD) evaluation score was 4.38 ± 0.30. The strongest positive correlation with mean evaluation score was for type size (r = 0.32; p = 0.01), whereas the strongest negative association was for number of clinical cases presented (r = -0.32; p = 0.01). No association with percentage of text slides (r = 0.19; p = 0.14) or mean number of images on an image slide (r = -0.22; p = 0.08) was identified. Content analysis revealed a moderate positive correlation between percentage of total slides containing text only and the percentage of positive comments (r = 0.31) and a weak correlation between the mean number of images per image slide and the percentage of negative comments (r = 0.24). CONCLUSION: Larger type size and a higher proportion of text slides were more favored by medical students.
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 | 0.019 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".