Triggers and Coping Strategies for Fear of Cancer Recurrence in Cancer Survivors: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Fear of cancer recurrence (FCR) has been demonstrated to be one of the most frequently reported unmet psychological needs among cancer survivors. The aim of this study was to explore and describe the potential triggers and coping strategies for FCR in Chinese cancer survivors. METHODS: The study process was conducted using an interpretive phenomenological research method, and Chinese cancer survivors were interviewed face-to-face in a semi-structured interview, using purposive sampling combined with a maximum variance sampling strategy, and the interviews were transcribed, organized, and analyzed by applying Giorgi analysis with the help of NVivo11 software. RESULTS: A total of 10 participants, 4 males and 6 females, were interviewed. Three themes emerged in terms of potential triggers for FCR: (1) intrusive thoughts; (2) disease symptoms; and (3) awaiting medical examination. Two themes regarding positive coping and avoidance coping emerged with regard to coping strategies adopted by cancer survivors when experiencing FCR. Under these 2 themes were 5 sub-themes: (1) seeking medical support; (2) self-health management; (3) spiritual coping; (4) unaccompanied toleration; and (5) attention shifting. CONCLUSION: FCR as the most common psychological problem for cancer survivors, and it should be given more attention. Early identification and precise intervention for potential triggers of FCR may prevent the emergence and development of FCR. The guidance toward and cultivation of positive coping strategies when cancer survivors experience FCR could be an important direction in future nursing education.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".