Age and Ageing During the COVID-19 Pandemic; Challenges to Public Health and to the Health of the Public
Bibliographic record
Abstract
risk factor for hospital and ICU admission, mechanical ventilation and death. Health systems must protect persons at any age while paying particular attention to those with risk factors. However, essential freedoms must be respected and social/psychological needs met for those shielded. The example of the older population in Israel may provide interesting public health lessons. Relatively speaking, Israel is a demographically young country, with only 11.5% of its population 65 years and older as compared with the OECD average of >17%. As well, a lower proportion of older persons is in long-term institutions in Israel than in most other OECD countries. The initiation of a national program to protect older residents of nursing homes and more latterly, a successful vaccine program has resulted in relatively low rates of serious COVID-19 related disease and mortality in Israel. However, the global situation remains unstable and the older population remains at risk. The rollout of efficacious vaccines is in progress but it will probably take years to cover the world's population, especially those living in low- and middle-income countries. Every effort must be made not to leave these poorer countries behind. Marrying the principles of public health (care of the population) with those of geriatric medicine (care of the older individual) offers the best way forward.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".