Attitudes About Coping With Fatigue in Patients With Gastric Cancer
Bibliographic record
Abstract
Cancer-related fatigue is the most common symptom in patients with cancer. Coping methods for cancer-related fatigue differ from those of patients without cancer, as the situations faced by patients with cancer are unique. This study aimed to identify subjectivity concerning coping with fatigue in Korean patients with gastric cancer. Q-methodology was used to examine subjective perceptions regarding coping with fatigue among Korean patients with gastric cancer. A convenience sample of 33 participants, who had been hospitalized in 2 university hospitals in South Korea, was recruited to participate in the study and 37 selected Q-samples were classified into a normal forced distribution using a 9-point bipolar grid. The obtained data were analyzed by using PC-QUANL for Windows. Three factors representing distinct attitudes about coping with fatigue emerged among Korean patients with gastric cancer: an optimistic mind, dependency on medicine, and exercise preference. The 3 factors explained 39.4% of the total variance (23.7%, 7.9%, and 7.8%, respectively). Based on the study findings, it is important to develop customized nursing interventions that consider the characteristics of each patient group with gastric cancer. Health professionals should assess the attitudes of patients with gastric cancer about coping with fatigue, explore their situation, and consider their lifestyle.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".