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Record W3048362108 · doi:10.1097/jcn.0000000000000733

Views of Patients With Heart Failure on Their Value-Based Self-care Decisions

2020· article· en· W3048362108 on OpenAlexafffundabout
Mehri Karimi-Dehkordi, Alexander M. Clark

Bibliographic record

VenueThe Journal of Cardiovascular Nursing · 2020
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsSelf carePsychologyValue (mathematics)Heart failureMedicineGerontologyClinical psychologySocial psychologyHealth careCardiologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Self-care adherence remains low in patients with heart failure (HF); little is known about the influence of patients' values on self-care decisions and behaviors. OBJECTIVES: The aim of this study was to explore how participants living with HF perceive their values and how those values are reportedly expressed in self-care decision making. METHODS: The Interpretative Phenomenological Analysis approach was used. Semistructured interviews were conducted with 12 patients 60 years or older; with New York Heart Association class II and III HF; and able to speak, read, and understand English. Participants recruited via convenience sampling (January-December 2016) from 2 urban sites in Western Canada. RESULTS: Values were reported to pivotally influence HF self-care decisions and behaviors. Overarching themes addressed aspects of values and decision making: notably, directness and complexity. Two main types of values, functional and emotional values, were involved in both adherent and nonadherent decisions. Values were often in flux, with the pursuit of these values being frequently in conflict with physical ability and changing over time. CONCLUSION: Two types of values serve influence self-care decisions and adherence. As HF and its self-care prevent patients from pursuing their prioritized values, patients are often nonadherent. Thus, patients with HF should be supported to find alternative ways to enact their values.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.021
GPT teacher head0.248
Teacher spread0.226 · 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 designNot applicable
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
Published2020
Admission routes3
Has abstractyes

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