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Record W2801192702 · doi:10.1177/0046958018766667

Medium-Term Health of Seniors Following Exposure to a Natural Disaster

2018· article· en· W2801192702 on OpenAlexaff
Óscar Labra, Danielle Maltais, Gabriel Gingras-Lacroix

Bibliographic record

VenueINQUIRY The Journal of Health Care Organization Provision and Financing · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversité du Québec à ChicoutimiUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsNatural disasterPovertyEnvironmental healthMedicineNatural (archaeology)GerontologyPsychologyGeographyEconomic growth

Abstract

fetched live from OpenAlex

The article aims to describe the medium-term impacts of a major earthquake event (Chile, February 27, 2010) on 26 seniors. The authors adopted a qualitative study approach. Data obtained using the Impact of Event Scale-Revised (IES-R) show the presence of manifestations of posttraumatic stress in the majority of respondents. In addition, data collected in interviews demonstrated a progressive deterioration of the health of respondents over a period of 4 years following the disaster. Seniors are particularly vulnerable to the effects of material loss, emotional stress, and postdisaster health complications. These impacts are exacerbated by low economic status. Furthermore, broader research is necessary involving elderly living in poverty who have survived natural disasters and others without such experiences, in order to better identify and differentiate between health complications associated with exposure to disaster events and those linked more strictly with natural aging processes.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.381
Teacher spread0.356 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2018
Admission routes1
Has abstractyes

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Same venueINQUIRY The Journal of Health Care Organization Provision and FinancingSame topicDisaster Response and ManagementFrench-language works237,207