The Role of Resilience in Coping With Future Uncertainty Among People With Brain Tumors: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Adults with brain tumors learn to navigate unpredictable physical and psychological symptoms along with the possibilities of tumor recurrence. As a result, they tend to become resilient to confronting profound uncertainty and actively employ coping strategies. Yet, the impact of resilience on coping strategies among people with brain tumors has not been fully explored. OBJECTIVE: This study aimed to examine the effects of resilience on the association between future uncertainty and two distinct types of coping strategies (problem-focused coping and emotion-focused coping) among people with brain tumors in Taiwan. METHODS: A parent study recruited 95 adults with brain tumors undergoing at least 1 month of chemotherapy or radiotherapy at a veterans general hospital in northern Taiwan. We assessed resilience, future uncertainty, and coping strategies via a secondary analysis of data from the parent study collected using the Chinese versions of the Resilience Scale, the European Organization for Research and Treatment of Cancer's Quality of Life Questionnaire for brain cancer, and the revised Ways of Coping Checklist. Simple mediation models were conducted to examine the role of resilience between future uncertainty and the two types of coping strategies. RESULTS: Most participants demonstrated low resilience and responded to stress with both problem- and emotion-focused coping strategies. Simple mediation analyses showed a statistically significant association between an increase in resilience and adoption of each type of coping strategy. In addition, resilience was a statistically significant mediator in the association between future uncertainty and both problem- and emotion-focused coping strategies. CONCLUSIONS: Brain tumor disease trajectories require people to effectively adopt both problem- and emotion-focused coping strategies to confront uncertainty. Health care providers play a crucial role in evaluating and fostering their patients' resilience to promote adaptability through effective coping strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".