Acceptance of illness of advanced cancer patients in palliative care center in Kuwait.
Bibliographic record
Abstract
e21501 Background: Cancer is a life threatening disease and has an enormous effect on patient’s physical, mental and social aspects. Acceptance of illness can reduce the negative emotions associated with the disease and its complications thus could help to improve patient’s quality of life. The palliative care center of Kuwait (PCC–K) was established a few years ago as the first stand-alone PC center in the region. So the aim of the study was to study is to explore the relationship between patient psychological acceptance of his illness and QOL thus his physical, functional and psychological problems. Methods: A cross sectional survey was conducted on 50 patients with advanced cancer receiving palliative managements. Patients were counseled after 7 days of admission to complete the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C15-pal and The Edmonton Symptom Assessment System (ESAS) Acceptance of Illness questionnaire through patient’s interview. Results: Mean age was 57.8 ± 8.5 years. 52% (n = 26) of the patients was males and 48% (n = 24) was females. The most common cancer diagnosis was lung cancer (16%, n = 8) followed by colonic cancer and breast cancer, each is 12% (n = 6). By using the Acceptance of Illness questionnaire, 14% of the patients were not accepting their illness while 42% was moderate acceptance and finally 44% was good acceptance of illness. Conclusions: Assessment acceptance of illness in palliative care is likely to disclose important information about patients’ psychology which can help the physician to elucidate the plan of management of the patients. Keywords: Cancer, Acceptance, Palliative 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".