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Record W2140560969 · doi:10.3390/healthcare3040973

“I Just Can’t Do It Anymore” Patterns of Physical Activity and Cardiac Rehabilitation in African Americans with Heart Failure: A Mixed Method Study

2015· article· en· W2140560969 on OpenAlexfundno aff
Margaret M. McCarthy, Stuart D. Katz, Judith Schipper, Victoria Vaughan Dickson

Bibliographic record

VenueHealthcare · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
FundersYork University
KeywordsHeart failureRehabilitationPhysical activityPsychologyMedicinePhysical medicine and rehabilitationPhysical therapyGerontologyCardiology

Abstract

fetched live from OpenAlex

Physical activity and cardiac rehabilitation (CR) are components of heart failure (HF) self-care. The aims of this study were to describe patterns of physical activity in African Americans (n = 30) with HF and to explore experience in CR. This was a mixed method, concurrent nested, predominantly qualitative study. Qualitative data were collected via interviews exploring typical physical activity, and CR experience. It was augmented by quantitative data measuring HF severity, self-care, functional capacity and depressive symptoms. Mean age was 60 ± 15 years; 65% were New York Heart Association (NYHA) class III HF. Forty-three percent reported that they did less than 30 min of exercise in the past week; 23% were told "nothing" about exercise by their provider, and 53% were told to do "minimal exercise". A measure of functional capacity indicated the ability to do moderate activity. Two related themes stemmed from the narratives describing current physical activity: "given up" and "still trying". Six participants recalled referral to CR with one person participating. There was high concordance between qualitative and quantitative data, and evidence that depression may play a role in low levels of physical activity. Findings highlight the need for strategies to increase adherence to current physical activity guidelines in this older minority population with HF.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.050
GPT teacher head0.406
Teacher spread0.356 · 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
Published2015
Admission routes1
Has abstractyes

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