A Public Health Emergency Simulation Tool for Enhanced Training in Emergency Preparedness and Response
Bibliographic record
Abstract
Study/Objective: To reveal the pattern of student engagement (the amount of time a student logged in) in Public Health Principles in Disaster and Medical Humanitarian Response (PHPID) online course, and to examine whether the pattern is associated with the course outcome (the probability of certificate attainment).Background: Student enrollment in online courses has increased in the past decade and continues to grow.Online courses become an effective platform to teach students globally in public health and disaster.However, how students engage in, and how the engagement pattern is associated within the course outcomes, was unknown.Methods: This research collected registration information and time-stamped Model login data from four completed cohorts of PHPID online courses (2014)(2015)(2016).Descriptive analysis, chisquare test, and multiple logistic regression were conducted via SPSS.Results: In total, 3,457 participants, from 150+ different countries registered, and 20.6% had passed the examination and obtained certificates.On average, each student spent 4.3 hours, 15.7 hours for certificate obtainers, and 1.3 hours for noncertificate obtainers.Males invested 18.3% more time than females.The participants with qualification in public health or medicine spent 30.7% more time than others.The student engagement was confirmed to have a significant and strong effect on their course completion, and in obtaining certificates, with adjusting gender, age, and education level (AOR = 1.401; 95%CI, 1.367-1.436).Conclusion: The patterns of student engagement in PHPID online courses were varied, associated with socio-demographic variables.Spent more hours in able to increase the probability of course completion and certification obtainment.Further research should be conducted to meet the needs of online course training in disaster and public health education.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".