Stress and coping with advanced cancer
Bibliographic record
Abstract
OBJECTIVE: For people with advanced cancer, the months preceding death can be very stressful. Moreover, cancer-related stressors can arise within multiple dimensions. However, little research has examined how people cope differentially with different types of stressors. The goal of this study was to examine patterns of coping across different dimensions of stress. METHOD: Fifty-two patients who were receiving palliative care for cancer were asked to indicate their most significant stressors within social, physical, and existential dimensions. A structured interview was then conducted to describe how the participants coped with these stressors. RESULTS: Overall, stressor severity ratings were correlated significantly across the three dimensions, although physical symptoms received the highest mean rating. Participants generally used a range of coping strategies to deal with their stressors, but there were clear differences across dimensions in the relative use of problem-focused versus emotion-focused strategies. Problem-focused coping was less frequent for existential issues, whereas emotion-focused strategies were used less frequently for physical stressors. Coping efforts were not clearly related to psychological distress. SIGNIFICANCE OF RESULTS: Although coping is an important research theme within psycho-oncology, it may be overly broad to ask, "How do people cope with cancer"? In fact, different cancer-related stressors are coped with in very different ways. There is not necessarily any particular pattern of coping that is best for relieving psychological distress.
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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 | 0.002 |
| 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 | 0.001 |
| 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".