To lecture or not to lecture, that is the question! Modern medical and nursing students' perceptions regarding lectures and lecture attendance at the University of Ottawa
Bibliographic record
Abstract
Lecture has historically been a core method used for content delivery in healthcare profession education. However, lecture attendance has decreased within the recent generations of students. The current study focus was to assess the medical and nursing students’ perceptions regarding lecture attendance. To assist with this, second year medical (110/320) and nursing students (95/215) were requested to answer a 10-item survey. The results show that the top reasons why medical and nursing students attended lectures, respectively included: “lectures were mandatory” (81.8% and 68.8%), “socializing with peers” (68.2% and 30.1%), and “professor emphasized important points” (67.3% and 90.3%). While some reasons for students not attending lectures were that the lecture format was not effective (63.5% and 67.7%), students preferred to use recordings of the lectures (43.3% and 18.1%). Overall, 64.6% of medical students and 63.4% of nursing students agree that traditional lectures are an effective way of learning. Sixty two percent of medical students (62% n=68) of medical students stated that traditional lectures is their preferred method of learning compared to flipped classroom (27%), small group learning (30%), and online learning (31%). While (39%) of nursing students stated that traditional lectures is their preferred method of learning compared to flipped classroom (21.5% ), small group learning (3.2%), and online learning (7.4%). The results suggest that there is variability in students’ preferred learning style. While some prefer the face-to-face interaction with the professor, other students favour studying at their own pace. The majority of medical and nursing students think traditional lectures continue to play a major educational role.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".