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Record W2740307419 · doi:10.1097/tgr.0000000000000152

Rural Older Adult Physical Activity Promotion

2017· article· en· W2740307419 on OpenAlexaffabout
Chad Witcher

Bibliographic record

VenueTopics in Geriatric Rehabilitation · 2017
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsGerontologyHealth promotionMedicinePromotion (chess)Physical activityQualitative researchRural areaPerceptionPublic healthPsychologyNursingSociologyPolitical sciencePhysical therapy

Abstract

fetched live from OpenAlex

The last 3 decades of research in exercise science have demonstrated the role of physical activity (PA) in maintaining, as well as improving, a variety of health outcome measures in older adults. However, rates of regular participation in PA remain relatively low. This is a significant public health issue, as inactive and insufficiently physically active older adults are more likely to develop chronic diseases such as heart disease, stroke, and diabetes. Furthermore, disparities in PA exist. For example, older adults in rural areas are less physically active than those in urban areas. Determining why such disparities in health and PA participation exist is a complex, but important endeavor and, presently, the underlying mechanisms are not well understood. By adopting interpretive research methodologies and methods (ie, “qualitative”), we can explore various contextual factors such as historical influences, social norms, and the cultural milieu of particular locations, which may influence area-specific participation in PA. Drawing upon research conducted in Canada, this article discusses the PA perceptions, preferences, and experiences of rural older adults, contextualizes these findings for practice, and considers to what extent a “rethink” of approaches to PA promotion may be necessary to serve future generations of rural older adults.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.025
GPT teacher head0.353
Teacher spread0.328 · 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

Citations8
Published2017
Admission routes2
Has abstractyes

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