Flipping Online Learning in Public Health Graduate Education
Bibliographic record
Abstract
The flipped classroom approach, used for many years in the humanities and the basic sciences, is becoming increasingly popular in public health education. This article describes the implementation and evaluation of a master’s-level Environmental and Occupational Health course, a required course in a Master of Public Health program at a mid-sized Canadian university. The course was designed using a flipped classroom approach and delivered online using a learning management system and interactive web-conferencing technology. Using a pre- and postsurvey design, we assessed improvements in student’s self-reported knowledge and skills, student learning experiences in the course, and the impact of specific course components on critical thinking and student engagement. Our results suggest that this approach enabled the achievement of course learning outcomes and provided positive learning experiences overall. Additionally, we find that the course promoted critical thinking and enabled student engagement in the context of online education for this small group of graduate-level public health students. We conclude by discussing key lessons learned for providing optimal learning experiences and outcomes in online graduate-level public health education.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".