The COVID-19 Pandemic and the Need for an Integrated and Equitable Approach: An International Expert Consensus Paper
Bibliographic record
Abstract
BACKGROUND: One year after the declaration of the coronavirus disease 2019 (COVID-19) pandemic by the World Health Organization (WHO) and despite the implementation of mandatory physical barriers and social distancing, humanity remains challenged by a long-lasting and devastating public health crisis. MANAGEMENT: Non-pharmacological interventions (NPIs) are efficient mitigation strategies. The success of these NPIs is dependent on the approval and commitment of the population. The launch of a mass vaccination program in many countries in late December 2020 with mRNA vaccines, adenovirus-based vaccines, and inactivated virus vaccines has generated hope for the end of the pandemic. CURRENT ISSUES: The continuous appearance of new pathogenic viral strains and the ability of vaccines to prevent infection and transmission raise important concerns as we try to achieve community immunity against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) and its variants. The need of a second and even third generation of vaccines has already been acknowledged by the WHO and governments. PERSPECTIVES: of medical care (PDA). CONCLUSION: The "PDA strategy" integrated into state policy for the support and expansion of health systems and introduction of digital organizations (i.e., telemedicine, e-Health, artificial intelligence, and machine-learning technology) is of major importance for the preservation of citizens' health and life world-wide.
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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 | 0.061 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".