Symptom Clusters and Mindful Self-Care in People with Cancer in Palliative Care
Bibliographic record
Abstract
Introduction: Cancer is one of the evils of the current era and is considered a global public health problem. This disease has repercussions for the lives of patients in several dimensions, namely, physical, emotional, and psychosocial. Thus, it is believed that elements such as resilience, symptomatology, and self-care are related, as the disease and its treatments can have repercussions that extend beyond the clinic. Background/Objectives: We aimed to determine the relationship between symptom clusters and the mindful self-care of people with cancer in palliative care. Methods: This is a cross-sectional study conducted with 125 palliative care patients diagnosed with malignant neoplasms. The research was carried out at a reference hospital in Brazil, located in the western region of the state of Santa Catarina, specializing in antineoplastic treatment. The data were collected between May and August 2023 from hospitalized patients. Three instruments were employed to obtain data: a sociodemographic and clinical data questionnaire, the Edmonton Symptom Assessment Scale (ESAS-BR), and the Mindful Self-Care Scale (MSCS). For data analysis, descriptive statistics were used to characterize the participants, Student’s T-test was used for the other parametric tests, and variables with statistical evidence were selected for a linear regression model. Results: A statistically significant association was found between mindful self-care and symptoms of pain, tiredness, drowsiness, shortness of breath, depression, and malaise, with sleepiness being the only predictor of changes in this variable. Conclusions: Mindful self-care influences patients’ experience of symptoms, especially drowsiness, which predicts changes in self-care. Encouraging these practices reduces discomfort, enhances autonomy, and guides professionals in personalized care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".