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Record W2416747849 · doi:10.1123/japa.19.4.322

Better Self-Perceived Health Is Associated With Lower Odds of Physical Inactivity in Older Adults With Chronic Disease

2011· article· en· W2416747849 on OpenAlexaffabout
Shilpa Dogra

Bibliographic record

VenueJournal of Aging and Physical Activity · 2011
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsAcadia University
Fundersnot available
KeywordsOddsChronic diseasePhysical activityGerontologyDiseaseMedicinePsychologyPhysical therapyLogistic regressionFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Poor self-perceived health (SPH) is associated with lower levels of physical activity (PA) and the presence of chronic disease in older adults. The purpose of this study was to determine whether SPH is associated with PA levels in older adults with existing chronic disease and whether this differs by disease. Using logistic regressions on data from the Canadian Community Health Survey (N = 33,168) it was found that adjusted logistic regressions revealed that odds of physical inactivity were similar in those with good SPH who reported having respiratory, musculoskeletal, or other chronic disease compared with those with good SPH without these diseases. Those with good SPH who reported having cardiometabolic disease were at significantly greater risk of physical inactivity than those with good SPH without cardiometabolic disease. It is apparent from the current analysis that SPH plays an important role in PA levels of older adults with chronic disease and should be targeted in future interventions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.303
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2011
Admission routes2
Has abstractyes

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