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Record W2159761162 · doi:10.1093/rpd/ncp082

When ageing and disasters collide: lessons from 16 international case studies

2009· article· en· W2159761162 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRadiation Protection Dosimetry · 2009
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsProsperityDisadvantagedNatural disasterDeveloping countryMainstreamEmergency managementOlder peoplePopulationPopulation ageingMedicineGerontologyEconomic growthPsychologyBusinessGeographyEnvironmental healthPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Sixteen case studies examined the impact of various natural disasters and conflict-related emergencies on older people, the strengths and gaps in emergency planning, response and recovery, and the contributions older people made to their families and communities. Case examples were chosen from both developed and developing countries. Older persons suffered disproportionate impacts in several cases. Regardless of the country's level of prosperity, those most affected tended to be economically disadvantaged, disabled or frail, women, socially isolated, or caregivers of family members. Emergency responders were often not aware of distinct needs or abilities of older persons and not equipped to respond appropriately. The best emergency practices recognised and included specific needs within mainstream efforts and integrated older persons in community planning, response and recovery activities. This paper presents the 'lessons learned' from these case studies and makes the case for greater attention to this segment of the population in emergency management.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.080
GPT teacher head0.427
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it