Education of Future Public Health Professionals Through Integrated Workshops
Bibliographic record
Abstract
An important feature of public health education is integrating and synthesizing complex concepts across a variety of disciplines. Novel and effective approaches are required to successfully integrate learning and knowledge across Master of Public Health (MPH) programs. The MPH program at Western University uses Integrated Workshops (IWs) as a unique approach to integrating learning and knowledge. Occurring three times over the course of the 1-year program, these workshops provide an opportunity to reflect on past learning and integrate interdisciplinary knowledge from across courses to solve a complex public health problem. IWs are designed for learners to explore the intricacies of a problem by synthesizing their current knowledge along with new information delivered from experts and stakeholders. Learners pull information from across subjects and seek out new information (as needed) to problem-solve under time constraints—basic information is provided 12 hours in advance and new information is added during the workshop, in real-time. Learners develop key public health skills in critical thinking and decision making with incomplete data. Integrated workshops are an effective approach to training the next generation of public health leaders to handle the intricate problems at the heart of public health today.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".