Epidemiology, Secondary School Curricula, and Preparing the Next Generation for Global Citizenship
Bibliographic record
Abstract
Because COVID-19 and other emerging infectious diseases are likely to play an increasingly important role in shaping American and global society in years to come, there is a need to prepare young people to make informed decisions in this changing global context. One way to do so is teaching and learning about basic principles of epidemiology in secondary schools. Improved understanding about the agent of infection, mechanisms of transmission, factors that increase or decrease susceptibility, place variation and environmental factors that facilitate or hinder transmission, reservoirs of infection (where the agent lives and multiplies), and when the disease is more or less likely to occur comprise the main facts about an infectious disease relevant to prevention and control. Improved understanding of these basic concepts could help future generations make informed decisions in a changing global context with emerging infectious diseases and a plethora of widely disseminated misinformation and disinformation. This viewpoint considers why learning about epidemiology in secondary school would benefit population health using COVID-19 as an illustration.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".