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Record W2166252993 · doi:10.1123/jpah.2011-0305

Physical Inactivity Among Older Canadian Adults

2013· article· en· W2166252993 on OpenAlexaffabout
Sunday Azagba, Mesbah Fathy Sharaf

Bibliographic record

VenueJournal of Physical Activity and Health · 2013
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsConcordia UniversityDalhousie University
Fundersnot available
KeywordsOddsDemographyLogistic regressionMedicineGerontologyOdds ratioPhysical activityPopulationImmigrationEnvironmental healthPhysical therapyGeography

Abstract

fetched live from OpenAlex

BACKGROUND: In spite of the substantial benefits of physical activity for healthy aging, older adults are considered the most physically inactive segment of the Canadian population. This paper examines leisure-time physical inactivity (LTPA) and its correlates among older Canadian adults. METHODS: We use data from the Canadian Community Health Survey with 45,265 individuals aged 50-79 years. A logistic regression is estimated and separate regressions are performed for males and females. RESULTS: About 50% of older Canadian adults are physically inactive. Higher odds of physical inactivity are found among current smokers (OR = 1.52, CI = 1.37-1.69), those who binge-drink (OR = 1.24, CI = 1.11-1.39), visible minorities (OR = 1.60, CI = 1.39-1.85), immigrants (OR = 1.13, CI = 1.02-1.25), individuals with high perceived life stress (OR = 1.48, CI = 1.31-1.66). We also find lower odds of physical inactivity among: males (OR = 0.89, CI = 0.83 to 0.96), those with strong social interaction (OR = 0.71, CI = 0.66-0.77), with general life satisfaction (OR = 0.66, CI = 0.58-0.76) and individuals with more education. Similar results are obtained from separate regressions for males and females. CONCLUSIONS: Identifying the correlates of LTPA among older adults can inform useful intervention measures.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.030
GPT teacher head0.332
Teacher spread0.302 · 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

Citations49
Published2013
Admission routes2
Has abstractyes

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