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Record W3193875657 · doi:10.1017/s0714980821000271

How Do Non-Catastrophic Natural Disasters Impact Middle-Aged-to-Older Persons? Using Baseline Canadian Longitudinal Study on Aging Data to Explore Psychological Outcomes Associated with the 2013 Calgary Flood

2021· article· en· W3193875657 on OpenAlexafffundabout
Samantha J. Norberg, Ann M. Toohey, David B. Hogan

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsFlood mythLongitudinal studyNatural disasterMental healthBaseline (sea)GerontologyMedicineAnxietyLogistic regressionDemographyLife satisfactionPsychologyPsychiatryGeography

Abstract

fetched live from OpenAlex

The objective of this study was to identify group-level health outcomes associated with the 2013 Calgary flood on Calgary participants (45-85 years of age) in the Canadian Longitudinal Study on Aging (CLSA). We compared baseline CLSA data collected on Calgary participants during the 6 months prior to and following the flood. Logistic regression models were created to explore whether select psychological outcomes were associated with the flood for participants categorized by evacuation status. Participants living in evacuated communities pre-flood had significantly lower levels of a diagnosed anxiety disorder than non-evacuated communities, which disappeared post-flood. Participants with higher household income were less likely to have post-traumatic stress disorder symptoms, worse self-rated mental health, and lower life satisfaction post-flood. Living alone reduced and female gender increased levels of perceived functional social support post-flood. Although natural disasters can shape research findings, the scope of the data being collected and the representativeness of impacted groups may challenge the ability to detect subtle impacts.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0020.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.094
GPT teacher head0.323
Teacher spread0.230 · 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 teacher head, not a consensus.

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

Citations3
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicDisaster Management and ResilienceFrench-language works237,207