Rethinking public health pedagogy: lessons learned and pertinent questions
Bibliographic record
Abstract
COVID-19 has, understandably, drastically shifted the way our world operates. Inevitably, the field of public health has experienced an explosion of innovation and learning opportunities. For instance, while health studies/public health university programs teach students about health from a social perspective, COVID-19 has afforded new lessons about the field of public health and considerations for educators. This manuscript explores cases of COVID-19 yielding new lessons for students, directly and indirectly, through the author’s position of teaching in the field across two institutions. For example, through the application of COVID-19 to policy theory, we are able to consider how COVID-19 may be a catalyst for policy change in the social determinants of health. Similarly, this manuscript discusses examples learned inadvertently through teaching. For example, the movement of instruction from in-person to online raises equity concerns by enhancing access to education for some, while restricting access to education for others; bringing equity considerations that are inherent in the field to the forefront of teaching. With regard to public health education, COVID-19 presents opportunity for pedagogical improvement both directly and indirectly. However, we must ask ourselves how much reliance on COVID-19 as a topic and a tool for education is too much? COVID-19 has infiltrated essentially every major facet of daily life; should it also be incorporated into nearly all of our lessons? In this manuscript, we present key areas and questions for the consideration of those who engage in public health education, which are applicable inside and outside the (possibly virtual) university classroom.
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.
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 | 0.041 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".