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The association between allostatic load and guaranteed annual income using the Canadian Longitudinal Study on Aging: A cross-sectional analysis of the benefits of guaranteed public pensions

2024· article· en· W4392979320 on OpenAlexafffundabout
Luke Duignan, Daniel J. Dutton

Bibliographic record

VenueHealth Policy · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health ResearchCanada Foundation for InnovationGovernment of Canada
KeywordsAllostatic loadPublic healthAllostasisCross-sectional studyLogistic regressionGerontologyHealth and Retirement StudyLongitudinal studyInequalityEnvironmental healthDemographic economicsPsychologyMedicineDemographyEconomicsSociology

Abstract

fetched live from OpenAlex

Old Age Security (OAS) represents an public policy through which income-related inequalities in health may be improved. The goal of this cross-sectional study was to investigate the health benefits of receiving OAS in financially insecure older Canadians. Using data from the Canadian Longitudinal Study on Aging (CLSA) (n=15,691), ordered logistic regression was used to measure associations between financial insecurity and allostatic load. Receiving OAS as highest personal income source appeared to remove the health penalty of being financially insecure. While financial insecurity was associated with worse allostatic load in both males and females not receiving OAS, those receiving OAS as highest personal income source had better allostatic load compared to other financially insecure older adults (ORM: 0.398, 95% CI: 0.227, 0.696; ORF: 0.677, 95% CI: 0.483, 0.949). While longitudinal data would be needed to draw causal inferences, these results suggest OAS may play a role improving health outcomes and narrowing income-related health inequalities. Such findings may have important implications on older adults, other vulnerable populations, and future directions of Canadian health and public policy.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0040.000
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 head0.140
GPT teacher head0.464
Teacher spread0.324 · 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
Published2024
Admission routes3
Has abstractyes

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