The Design of a Master of Public Health Professional Development Course During the COVID-19 Pandemic: Application of the Salmon Model
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) has highlighted the need for well-trained public health workers to interpret evidence, make informed decisions, and disseminate information to the general public. As public health courses in Ontario universities have moved online due to this pandemic, instructors were required to simulate their teaching online while maintaining student engagement. Previous research has shown that there is a lack of description for the development of online public health courses. As such, the objective of this article is to outline the development and layout of a Professional Development Studio course offered in the Masters of Public Health program at McMaster University, Hamilton, Ontario. We use the Salmon model, previously described by Salmon and colleagues in 2013, to form the course outline. The Salmon model provides a five-stage framework for the development of a concise, engaging, and impactful online course. Based on student feedback, we found that the Salmon model positively shaped the development of the course by aiding the formulation of a course layout that was easily accessible, discussion threads to communicate in an inclusive and safe space, and relevant assessments requiring the use of tools to make judgments and appropriately disseminate information publicly. We conclude that the Salmon model is a helpful framework to use in developing an engaging online public health course. Further assessments based on student feedback should be completed to continually evolve the online course to better tailor the needs and interests of public health students preparing them for the public health workforce.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".