Utilization of health care services in cancer patients with elevated fear of cancer recurrence
Bibliographic record
Abstract
BACKGROUND: Cancer patients commonly report experiencing fear of cancer recurrence (FCR), which may lead to several negative consequences. This study aimed at examining whether clinical levels of FCR are linked to a greater use of health care services. METHOD: This is a secondary analysis of a longitudinal study of 962 cancer patients on the epidemiology of cancer-related insomnia. They completed the Fear of Cancer Recurrence Inventory-Short form (FCRI-SF) and reported information on their consultations (medical, psychosocial, and complementary and alternative medicine [CAM]) and medication usage (anxiolytics/hypnotics and antidepressants) at 6 time points over an 18-month period. RESULTS: Results indicated that clinical FCR at baseline was associated with greater consultation rates of medical and psychosocial professionals and a greater usage of anxiolytics/hypnotics and antidepressants. No significant association was found between the FCR level and use of CAM services. While consultation rates of medical and CAM professionals and usage of antidepressants generally increased over time, consultation rates of psychosocial professionals and usage of anxiolytics/hypnotics tended to decrease. CONCLUSIONS: Cancer patients with clinical levels of FCR are more likely to consult health care providers and to use psychotropic medications, which may translate into significant costs for society and the patients themselves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".