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Record W2725991551 · doi:10.1093/geroni/igx004.2634

THE INDIVIDUAL AND STRUCTURAL DETERMINANTS OF AGING WELL IN CANADA AND MEXICO

2017· article· en· W2725991551 on OpenAlexaffabout
U. Perez-Zepeda, Roberto Carlos Castrejón‐Pérez, Emmanuelle Bélanger, Marı́a Victoria Zunzunegui

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSocioeconomic statusGerontologyIndex (typography)Healthy agingPresentation (obstetrics)Social determinants of healthPsychologyDemographyMedicineEnvironmental healthPublic healthSociologyPopulation

Abstract

fetched live from OpenAlex

The literature suggests that successful aging is largely determined by health behaviors and the social and physical environment of older adults. For this presentation, we examined the determinants of a person-centered index of aging well (0–100), as inspired by the determinants of the WHO concept of active aging, namely socioeconomic, behavioral, and environmental determinants. In both contexts, older participants were less likely to age well. Those with less than very sufficient income also had lower scores on the index. In Canada, smoking (past and present) was associated with lower scores, while physical exercise had a small but significant positive association. In Mexico, men aged well significantly more than women, as did those with more education and those who exercised at least 60 minutes a week. This presentation sheds light on modifiable determinants of aging well that can be addressed through health and social policies, particularly health behaviors and income sufficiency.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.957

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 studies0.0000.000
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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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