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Record W4367596492 · doi:10.3390/healthcare11091272

Cancer Patients’ Satisfaction with In-Home Palliative Care and Its Impact on Disease Symptoms

2023· article· en· W4367596492 on OpenAlexaboutno aff
Inmaculada Valero-Cantero, Cristina Casals, Milagrosa Espinar-Toledo, Francisco Javier Barón-López, Francisco Javier Martínez-Valero, María Ángeles Vázquez‐Sánchez

Bibliographic record

VenueHealthcare · 2023
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePalliative careQuality of life (healthcare)Patient satisfactionDepression (economics)DiseaseCross-sectional studyFamily medicineHealth carePhysical therapyNursingInternal medicine

Abstract

fetched live from OpenAlex

The aim of the study was to determine whether the satisfaction of cancer patients with in-home palliative care is associated with the impact of disease symptoms and with self-perceived quality of life. This was a cross-sectional descriptive study, conducted in the primary health care sector in six clinical management units, where 72 patients were recruited over a period of six months. The severity of symptoms was determined by the Edmonton Symptom Assessment System (ESAS). Quality of life was evaluated with the EORTC QLQ-C30 (version 3) questionnaire, and patients’ satisfaction with the care received was evaluated by the Client Satisfaction Questionnaire (CSQ-8). The patients’ satisfaction with the health care received was represented by an average score of 6, on a scale of 1–10; thus, there is room for improvement in patient satisfaction. Moreover, it was found that more intense symptoms and lower quality of life are associated with lower satisfaction with health care received (p = 0.001). Similarly, when symptoms are more severe, the quality of life is lower (p < 0.001). The identification of fatigue, reduced well-being, pain, drowsiness, and depression as the symptoms experienced with the highest intensity by our patients provides valuable information for health care providers in developing individualized symptom management plans for patients with advanced cancer.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.077
GPT teacher head0.445
Teacher spread0.367 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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