MétaCan
Menu
Back to cohort
Record W2162816584 · doi:10.1093/ageing/afn114

Psychosocial predictors of physical activity in older aged asthmatics

2008· article· en· W2162816584 on OpenAlexaffabout
Shiwangi Dogra, Brad A. Meisner, Joseph Baker

Bibliographic record

VenueAge and Ageing · 2008
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsYork University
Fundersnot available
KeywordsMedicinePsychosocialAsthmaLogistic regressionPsychological interventionPopulationCross-sectional studyGerontologyOlder peopleDemographyInternal medicinePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: there is little information available on physical activity (PA) patterns and the psychosocial determinants of PA in older adults with asthma. OBJECTIVE: to quantify the prevalence of PA in older asthmatics and to explore the potential psychosocial determinants of PA in this population. STUDY DESIGN AND SETTING: cross-sectional data available from the Canadian Community Health Survey (CCHS), cycle 2.1, were used. There was a total of 1,772 older asthmatics in the sample. RESULTS: there were significant differences in the prevalence of PA between older asthmatic females compared to middle-aged asthmatic females (chi(2) = 23.65, P < 0.0001) and older asthmatics compared to older non-asthmatics (chi(2) = 38.1, P < 0.0001). Logistic regression revealed a significant association between PA and perceived health in older asthmatic males (OR = 5.39, CI = 1.36-21.33) and females (OR = 4.81, CI = 1.41-16.38). Being a member of a volunteer organisation was also significantly associated with PA in older asthmatic females (OR = 1.59, CI = 1.11-2.30). CONCLUSION: older asthmatics were less active than their non-asthmatic peers. Perceived health was an important predictor of PA in both older asthmatic males and females. Exercise interventions in this population should make an effort to improve self-perceived health.

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 score0.800
Threshold uncertainty score0.233

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 head0.033
GPT teacher head0.313
Teacher spread0.280 · 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

Citations15
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueAge and AgeingSame topicPhysical Activity and HealthFrench-language works237,207