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Record W4409583405 · doi:10.1016/j.jeoa.2025.100564

Evaluating the relationship between income, survival and loss of autonomy among older Canadians

2025· article· en· W4409583405 on OpenAlexafffundabout
Marie‐Louise Leroux, Akakpo Konou

Bibliographic record

VenueThe Journal of the Economics of Ageing · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsSimon Fraser UniversityCenter for Interuniversity Research and Analysis on OrganizationsUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et CultureFonds de recherche du Québec
KeywordsAutonomyPsychologyGerontologyDemographic economicsEconomicsDemographyMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

Evaluating the relationship between health at old age and income is key for the design of equitable public policies targeted toward the elderly. While the health economics literature studying the relationship between income and survival is abundant, the literature studying the relationship between income and the risks to become dependent is still quite scarce. Using 2016 Canadian survey data on adults aged between 50 and 70, we find that income and the (objective and subjective) probability to live to age 85 are positively related while income and the (objective and subjective) probability to suffer from ADL limitations are negatively related. We also find that while the objective probability to enter a nursing home is negatively correlated with income, the subjective probability is positively correlated with income. Our results call for important policy recommendations. Poorer individuals are those who are more likely to become dependent and as such, long-term care (LTC) public policies should primarily be targeted toward them. This would generate a double benefit: first, by reducing the expected cost of dependency for those who would have more difficulty to pay for LTC expenditures and second, by fostering redistribution and decreasing income inequalities across the elderly.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.020
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 head0.063
GPT teacher head0.366
Teacher spread0.303 · 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.

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

Citations1
Published2025
Admission routes3
Has abstractyes

Explore more

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