Psychometric testing of the <scp>Fear of Cancer Recurrence Inventory</scp>‐caregiver <scp>Chinese</scp> version in cancer family caregivers in <scp>Taiwan</scp>
Bibliographic record
Abstract
OBJECTIVE: The purposes of this study were to develop a Chinese version of the Fear of Cancer Recurrence Inventory-caregiver (FCRI-c Chinese) and assess the psychometrics of this test in the family caregivers (FCs) of Taiwanese patients with head and neck cancer. METHODS: An instrument testing study was conducted at a major medical center in Taiwan. Head and neck cancer patients and their major FCs were recruited as dyads from the radiation outpatient department. The FCRI-c Chinese was tested for internal consistency reliability, test-retest reliability, and construct validity (including theoretically supported correlation, discriminant validity, and factor structure). RESULTS: We recruited 300 patient-caregiver dyads. The test had good internal consistency (Cronbach α = .94) and a 2-week test-retest reliability of .88. Confirmatory factor analysis indicated an acceptable fit of the model to the data. The construct validity was also satisfactory, as indicated by the significant positive correlations of the test with depression and anxiety in FCs, and the significant negative correlation of the test with patients' quality of life. A significantly higher test score was present in FCs caring for patients with metastasis and patients who completed treatment a long time ago. CONCLUSIONS: The FCRI-c Chinese is a valid instrument for examination of the fear of cancer recurrence in the FCs of patients with head and neck cancer. Clinicians can use this multidimensional instrument to assess important clinical care issues and improve the quality of care provided by FCs.
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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 | 0.004 | 0.011 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".