COVID Vaccine Rollout for Older People: East Meets West
Bibliographic record
Abstract
Abstract Older adults should be one of the first groups to receive COVID-19 vaccines, because the risk of dying from COVID-19 increases with age. However, it takes time to distribute the vaccines to different countries, and the challenges in administering vaccines may differ by health system characteristics and local culture. This international symposium will discuss the vaccine rollout issues in eight countries (Isreal, Japan, South Korea, China, France, United Kingdom, Canada, and United States). We will use an interview and dialog format, instead of presentations. We will cover extensive topics including: Availability - What vaccines? Access, Acceptance, Caregivers – How are providers responding/handling caregivers wanting to be vaccinated?Cost/Financing Issues, Distribution Logistics/Transport/Safety, Lessons Learned, Mutations/Variants, Partnerships needed to vaccinate homebound patients (community partners; home health agencies, etc.), Who can/should provide vaccination? The situation with COVID-19 is still very fluid. Countries are at different stages of vaccinating older people. The chair didn't ask the speakers to write an abstract now, instead, the speakers will collect more information during the next few months and plan to have a prep meeting one month before the Annual Meeting.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".