How Is Health Promoting University Strategy to Handle the COVID-19 Pandemic?
Bibliographic record
Abstract
The current COVID-19 pandemic has affected many countries, including Indonesia. Many parties, including educational institutions, have to deal with pandemic conditions. This paper aims to describe how the academic institution, Jenderal Soedirman University, is handling the pandemic situation. Various activities led by the COVID-19 Unsoed Task Force undertook several efforts to respond to the pandemic, such as conducting active supervision for all academics, mentoring teams, educating, conducting real work lecture programs, and forming COVID-19 joint volunteer teams. During data monitoring, several activities were completed such as handling patients in surveillance, tracking, and follow-up. Other efforts in the education field were also carried out to keep running the activity, but with a joint security procedure COVID-19 as well as several policies set such as changing the way of teaching to be online. Best possible efforts have been made by the university to respond to the pandemic quickly and effectively.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".