The Persian Version of the Fear of Cancer Recurrence Inventory (FCRI): Translation and Evaluation of Its Psychometric Properties
Bibliographic record
Abstract
Background: This study aimed to translate and validate the Fear of Cancer Recurrence Inventory (FCRI) questionnaire into Persian and to investigate its psychometric properties. Methods: The FCRI was translated to Persian using a linguistic methodology according to WHO guidelines. A total of 450 breast cancer survivors who had the following inclusion criteria were included: time elapse of more than six months after the treatment prior to the study; absence ofobjective markers of recurrence, fluency in the Persian language, and signing the informed consent. Internal consistency was estimated with Cronbach's ? coefficient and test-retest reliability with Interclass correlation. Concurrent validity was estimated through Pearson’s correlation between the FCRI and Hospital Anxiety and Depression Scale (HADS). Principal component analysis (PCA) and confirmatory factor analysis (CFA) were employed to evaluate dimensionality. Results: The Persian version was acceptable for patients. The content validity index (CVI) was 0.80. The instrument had good test-retest reliability (ICC= 0.96) and internal consistency (Cronbach’s ?=0.86). PCA and CFA indicated that the factor structure of the Persian version was similar to the original questionnaire and had acceptable goodness of fit. Correlations between the FCRI and HADS was remarkable (r= 0.252 – 0.639), indicating acceptable concurrent validity. Conclusions: The Persian version of FCRI could be considered a good cross-cultural equivalent for the original English version. The questionnaire was a reliable and valid instrument in terms of internal consistency, test-retest reliability, and dimensionality.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.004 | 0.001 |
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".