Bibliographic record
Abstract
Abstract Frail elderly people are particularly vulnerable during hurricanes. Of the 1,330 people known to have perished along the Gulf Coast as a result of Hurricane Katrina, 71% of those in Louisiana were older than 60 years, 47% were older than 75 years, and at least 68 died in nursing homes. Unfortunately, community disaster planning frequently fails to allow for the needs of the frail elderly before, during, and after hurricanes. This paper discusses the particular vulnerabilities of the frail elderly, especially those with chronic diseases, those in residential care facilities, and those who are dialysis‐dependent. The importance of the Incident Management System (IMS) is discussed, and those who care for the frail elderly in long‐term care facilities must understand and use IMS in dealing with hurricane‐related disasters. Recommendations are made that will improve hurricane disaster planning for the frail elderly. From a policy viewpoint, it is critical that the elderly, especially those with chronic diseases, be included in disaster planning at the federal, state, and local levels to ensure that a repeat of the Hurricane Katrina debacle does not occur.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".