Fear of cancer recurrence among cancer survivors in Singapore
Bibliographic record
Abstract
INTRODUCTION: Fear of cancer recurrence (FCR) among cancer survivors is a persistent and distressing psychosocial concern that affects recovery and quality of life. The prevalence of FCR in Singapore is unknown. This cross-sectional study was designed to examine FCR and identify factors associated with FCR in mixed-cancer survivors locally. METHODS: Cancer survivors in remission (n = 404) were assessed for: FCR using the Fear of Cancer Recurrence Inventory (FCRI); emotional distress using the Hospital Anxiety and Depression Scale; and quality of life using the World Health Organization Quality of Life-BREF. Clinical and severe/pathological FCR was determined based on the severity scale of FCRI, known as FCRI-Short Form. Multivariate logistic regression was performed to examine factors associated with FCR. RESULTS: The mean score on the FCRI was 59.5 ± 30.4. 43.6% of cancer survivors had clinical FCR and 32.1% had severe/pathological FCR. Younger age (odds ratio [OR] 0.952, 95% confidence interval [CI] 0.911-0.995, p < 0.05), higher educational status (OR 2.55, 95% CI 1.15-5.65, p < 0.05) and higher levels of emotional distress (OR 1.17, 95% CI 1.10-1.24, p < 0.001) were significantly associated with severe/pathological levels of FCR. CONCLUSION: The present study is the first to determine levels of FCR among cancer survivors in Singapore. While the total FCR scores were similar to those of international studies, severe/pathological levels of FCR were found to be four times higher. These findings highlight a problem that is not widely recognised or acknowledged, but which deserves greater attention.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".