Fear of Cancer Recurrence Inventory : development and initial validation of a multidimensional measure of fear of cancer recurrence
Bibliographic record
Abstract
BACKGROUND: Despite the fact that the fear of cancer recurrence is to varying degrees almost universal in cancer survivors, there is a lack of validated multidimensional instruments to evaluate this issue specifically. \n \nPURPOSE: The goal of this study was to develop and empirically validate a multidimensional self-report scale for assessing the fear of cancer recurrence, the Fear of Cancer Recurrence Inventory (FCRI). \n \nMETHODS: A provincial medical databank was used to randomly select a pool of 1,704 French-Canadian patients who had been treated for breast, prostate, lung, and colorectal cancer within the past 10 years. Of these, 300 patients were asked to complete the FCRI on two occasions. \n \nRESULTS: The factorial analysis conducted on the final 42-item scale revealed a seven-component solution (64% of the variance) including the following factors: triggers, severity, psychological distress, coping strategies, functioning impairments, insight, and reassurance. The results also supported the internal consistency (alpha = 0.95) and the temporal stability (r = 0.89) of the FCRI, as well as its construct validity with other self-report scales assessing fear of cancer recurrence (r = 0.68 to 0.77) or related constructs such as psychological distress (r = 0.43 to 0.77) and quality of life (r = -0.20 to -0.36). \n \nCONCLUSIONS: This study suggests that the French-Canadian version of the FCRI is a reliable and valid instrument for evaluating the multidimensional aspects of the fear of cancer recurrence.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".