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Record W4317033782 · doi:10.15649/cuidarte.2539

Necesidades de cuidado paliativo del paciente con falla cardiaca: un estudio mixto

2022· article· es· W4317033782 on OpenAlexaboutno aff
Lucely Marisel Fiscal Idrobo, Priscilla Ospina-Muñoz, Lina María Vargas-Escobar, Maria Cilia Rincon Buenhombre

Bibliographic record

VenueRevista CUIDARTE · 2022
Typearticle
Languagees
FieldHealth Professions
TopicNursing care and research
Canadian institutionsnot available
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

Introduction: The presence of physical, psychosocial, and spiritual signs and symptoms should be identified and managed through the palliative care that health care teams and professionals pro vide. Objective: To identify the palliative care needs of people with heart failure, their caregivers, and the multidisciplinary team of a heart failure unit. Materials and Methods: A mixed-method study with a sequential transformative design (DITRAS, for its acronym in Spanish) was conducted. It began with a quantitative phase in which the Edmonton Symptom Assessment Scale (ESAS), the Therapy-Spiritual Well-Being Scale (FACIT-Sp-12), and the Barthel Index were used. The qualitative phase was conducted with three focus groups involving seven patients, eight caregivers, and twelve health professionals from the multidisciplinary team. Elizabeth Lenz's Theory of Unpleasant Symptoms was used as a guideline for this study. Results: Physiological (edema, fatigue, and dyspnea) and psychological (attitude towards life and enjoyment of hobbies) palliative care needs and situational factors (caregiver dependence and support networks) were identified and could be understood through Lenz's theory. Conclusions: Palliative care needs in patients with heart failure are presented under a framework of symptoms that patients, caregivers, and the health care team perceive. Comprehensive approaches are required to improve symptom experience.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
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 head0.047
GPT teacher head0.409
Teacher spread0.362 · 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 designQualitative
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

Citations4
Published2022
Admission routes1
Has abstractyes

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Same venueRevista CUIDARTESame topicNursing care and researchFrench-language works237,207