Fear of Cancer Recurrence and Coping Strategies among Prostate Cancer Survivors: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Fear of cancer recurrence (FCR), as a commonly reported problem among prostate cancer survivors, has not been fully understood. This study aimed to explore the experience of FCR and relevant coping strategies among Iranian prostate cancer survivors. METHODS: Qualitative research was conducted on 13 men who completed treatments for prostate cancer in the last 24 months. The participants were selected through purposeful sampling, and in-depth semi-structured interviews were used for data collection. Conventional content analysis was used for data analysis. RESULTS: Data analysis led to the emergence of three themes. "Living with insecurity" describes the participants' experiences regarding what triggers FCR with two categories, including "fear of incomplete cure" and "fear of cancer return." In addition, "struggling to cope" with two categories, including "psychological strategies" and "spiritual coping," presents coping strategies used by the participants for reducing FCR. Furthermore, "trying to prevent cancer recurrence" with two categories, "seeking health" and "lifestyle modification," indicates coping strategies used by the participants to prevent cancer recurrence. CONCLUSIONS: Healthcare providers need to consider the cultural characteristics of prostate cancer survivors when assessing their FCR, encourage them to disclose their concerns and fears, and provide tailored interventions in order to reduce FCR among them.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 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".