(A133) Emergency Response and Vulnerable Older People: Some Keys for Better Practices
Bibliographic record
Abstract
Emergency response and vulnerable older people: some keys for better practices Danielle Maltais, Ph.D. professor and Taha-Abderrafie Maala, M.Sc student, Social Work Teaching Unit, Department of Human Sciences, University of Quebec in Chicoutimi (UQAC). In the event of a natural or technological disaster, certain groups of people, some of elderly, are more vulnerable than others because they do not have easy access to the community resources. For example, several older people, especially those with a physical or cognitive incapacity and those with a low income, do not generally have a car available which can hinder their evacuation during a flood, an earthquake or a hurricane. Moreover, several elders live in older buildings not built to resist to shocks of all kinds. Older people, particularly those with a physical or cognitive incapacity, those with a low income or those without a social network belong to groups at risk to undergo wounds, to die or develop post-disaster health problems. Considering this, several researchers and national or international government and private as well as non-profit organizations such as World Health Organization, the International Red Cross or HelpAge International produced several guides on intervention aiming to support workers caring for the elderly during a disaster. The purpose of this communication is to present the main outstanding facts and recommendations of these various documents in order to heighten the participants' awareness of the importance to take into account the specificities of older people during the application of emergency measures and the recovery period of a community.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".