Coronavirus Disease 2019 (COVID-19) Crisis Measures: Health Protective Properties?
Bibliographic record
Abstract
The ongoing 2019 coronavirus disease (COVID-19) crisis has led governments to impose measures including mask wearing, physical distancing, and increased hygiene and disinfection, combined with home confinement and economic shutdown. Such measures have heavy negative consequences both on public health and the economy. However, these same measures have positive outcomes as "side effects" that are worth mentioning since they contribute to the improvement of some aspects of the population health. For instance, mask wearing helps to reduce allergies as well as the transmission of other airborne disease-causing pathogens. Physical distancing and social contact limitation help limit the spread of communicable diseases, and economic shutdown can reduce pollution and the health problems related to it. Decision makers could get inspired by these positive "side effects" to tackle and prevent diseases like allergies, infectious diseases and noncommunicable diseases, and improve health care and pathology management. Indeed, the effectiveness of such measures in tackling certain health problems encourages inspiration from COVID-19 measures towards managing selected health problems. However, with the massive damage COVID-19-related measures have caused to countries' economies and people's lives, the question of how to balance the advantages and disadvantages of these measures in order to further optimize them needs to be debated among health care professionals and decision makers.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".