Education of Future Public Health Professionals Through Integrated Workshops
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it