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Record W2111063743 · doi:10.1136/heartjnl-2013-304852

Determinants of effective heart failure self-care: a systematic review of patients’ and caregivers’ perceptions

2014· review· en· W2111063743 on OpenAlexafffund
Alexander M. Clark, Melisa A. Spaling, Karen Harkness, Judith A. Spiers, Patricia H. Strachan, David R. Thompson, Kay Currie

Bibliographic record

VenueHeart · 2014
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsMcMaster UniversityUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicinePsychological interventionIntervention (counseling)Context (archaeology)Heart failureDisease managementIntensive care medicineDiseaseNursingCardiologyInternal medicine

Abstract

fetched live from OpenAlex

CONTEXT: Disease management interventions for heart failure (HF) are inconsistent and very seldom incorporate the views and needs of patients and their caregivers into intervention design. OBJECTIVE AND DATA: To improve intervention effectiveness and consistency, a systematic review identified 49 studies which examined the views and needs of patients with HF and their caregivers about the nature and determinants of effective HF self-care. RESULTS: The findings identify key drivers of effective self-care, such as the capacity of patients to successfully integrate self-care practices with their preferred normal daily life patterns and recognise and respond to HF symptoms in a timely manner. CONCLUSIONS: Future interventions for HF self-care must involve family members throughout the intervention and harness patients' normal daily routines.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.046
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.303
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations157
Published2014
Admission routes2
Has abstractyes

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