The interconnectedness of fear of cancer recurrence components: A network approach.
Bibliographic record
Abstract
OBJECTIVE: While nearly 60% of cancer survivors report a heightened fear of cancer recurrence (FCR), not all of them experience functional impairment and want professional psychological care. We applied the network approach to study how different components of FCR (symptoms, triggers, perceived risk, and coping strategies) are interconnected to both FCR severity and functional impairment to better understand which survivors are likely to require psychological care. METHOD: We applied network analysis to cross-sectional data from 3,370 cancer survivors from nine different countries, spanning Asia, Australia, Europe, and North America, from the international Fear of Cancer Recurrence Inventory database. The shortest path analysis was applied to study what components were directly connected to both FCR severity and functional impairment. RESULTS: FCR severity was mainly connected to symptoms and triggers while functional impairment was mainly connected to coping strategies. The shortest paths indicated that worry and bodily triggers were directly connected to both higher FCR severity and more functional impairment. CONCLUSION: Worry and bodily triggers appear to be core components of FCR that are experienced as impairing in daily life. Our findings suggest that assessing functional impairment, worry, and bodily triggers, in addition to FCR severity, could be valuable when screening for clinical levels of FCR. To further improve our conceptual understanding of FCR, future studies should apply intensive longitudinal designs to explore how these components interact over time and within the individual. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".