Bibliographic record
Abstract
Background: Online distance education is proving to be an effective pedagogical vehicle for enhancing learning outcomes of medical students. Epidemiology is a subject area underexplored as an appropriate content area for online delivery. We sought to determine whether online epidemiology videos were an effective companion teaching tool that could result in measurable improvements in student performance. Methods: In partnership with the Lecturio Corporation in Germany, an online Epidemiology class created, featuring 11 recorded lectures tailored to the USMLE medical licensing requirements. Free access to the lectures was offered to the students of a 4th year undergraduate Epidemiology course at the University of Ottawa. Improvements in this group from the midterm examination baseline to the final examination performance were assessed relative to improvements experienced by students who did not watch the videos. Results: Students who watched the videos saw their average mark increased by 1.2%, while the mark in the control group decreased by 2.6%, though this difference was not statistically significant. Qualitative comments were universally positive with respect to the instructional usefulness of the videos. Conclusions: Augmenting traditional university epidemiology courses with an online video component is an effective strategy that can result in improved student learning outcomes, though further research is needed to determine how best to deploy such tools either in isolation or in partnership with in-person instruction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".