Immunization during COVID-19: let the ninja dance with the dragon
Bibliographic record
Abstract
Purpose Throughout history, pandemics have played a significant role in reshaping human civilizations through mortalities, morbidities, economic losses and other catastrophic consequences. The present COVID-19 pandemic has brought the world to its knees resulting in overstretched healthcare systems, increased health inequalities and disruptions to people’s right to health including life-saving routine immunization programs across the world. Design/methodology/approach This is a commentary paper. Findings Immunization remains one of the most successful, safe, cost-effective and proven fundamental disease prevention measures in the history of public health. However, the COVID-19 pandemic has effectively thrown the world's immunization practices out of gear, depriving approximately 80 million infants, in rich and poor countries alike, at risk of triggering a resurgence of vaccine-preventable diseases such as diphtheria, measles and polio. It is estimated that each COVID-19 death averted by suspending immunization sessions in Africa could lead to 29-347 future deaths due to other diseases including measles, yellow fever, polio, meningitis, pneumonia and diarrhoea. Originality/value The value of implementing robust immunization policies cannot be underestimated. Risks associated with postponing immunization services and the fact that COVID-19 is now an integral part of human civilization have resulted in several countries making special efforts to continue their immunization services. However, critical precautionary measures are warranted to prevent COVID-19 among healthcare service providers, facilitators, caregivers and children during the immunization sessions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 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".