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Record W2012057586 · doi:10.1002/pon.1497

Rural breast cancer survivors: exercise preferences and their determinants

2009· article· en· W2012057586 on OpenAlexaff
Laura Q. Rogers, Stephen Markwell, Steven J. Verhulst, Edward McAuley, Kerry S. Courneya

Bibliographic record

VenuePsycho-Oncology · 2009
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Alberta
FundersNational Institute on Aging
KeywordsPsychological interventionMedicinePopulationBreast cancerRural areaGerontologyPhysical therapyCancerEnvironmental healthNursingInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: As a first step in planning interventions to promote exercise in rural breast cancer survivors (BCS), we sought to determine the exercise preferences of rural BCS and to identify the major determinants of these preferences. METHODS: Self-administered mail survey to a population-based sample from a state cancer registry. RESULTS: Among the 483 respondents, 96% were White with mean education of 13+/-2.5 years and mean months since diagnosis of 39.0+/-21.5. Only 19% reported >or=150 min of moderate to vigorous physical activity per week. Although up to half were open to various counseling options, the most popular options were counseling after treatment (36%), face-to-face (47%), and from an exercise specialist (40%). Rural BCS preferred home-based (63%), unsupervised (47%), moderate intensity exercise (65%) that was primarily walking. The strongest preference correlates include higher education with exercise specialist, higher environment score with outdoors, more comorbidities with low intensity and counseling after cancer treatment, higher social support with exercising with friends or family, sedentary or insufficient physical activity with low intensity, and lower household income with preferring supervised exercise. CONCLUSIONS: Interventions designed to promote exercise among rural BCS are needed. Such interventions should consider the environmental aspects of this population and include multiple options based on the preferences of targeted subgroups.

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 score0.890
Threshold uncertainty score0.698

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 communication0.0000.000
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 head0.022
GPT teacher head0.334
Teacher spread0.312 · 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

Citations139
Published2009
Admission routes1
Has abstractyes

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