Examining the dimensionality of the Fear of Cancer Recurrence Inventory
Bibliographic record
Abstract
Abstract Objective Fear of cancer recurrence (FCR) is a common concern among cancer survivors, and the Fear of Cancer Recurrence Inventory (FCRI) is a frequently used measure to assess FCR. Given that the dimensionality of FCR has received recent debate, the overall goal of this secondary analysis was to re‐examine the dimensionality of the FCRI using confirmatory factor analyses (CFA) to compare models of FCR, using data from a large sample of cancer survivors. Methods Three models of FCR (including unidimensional and multidimensional models of the FCRI) were informed by the literature and proposed a priori. Separate CFAs were conducted to test the fit of each model to the data, and models with acceptable fits were compared. Results Of all the tested FCR models, a multidimensional first‐order model aligned with the originally developed 7‐subscale FCRI revealed the best fit to the data ( χ 2 = 3359.135, P < .0001, df = 795, RMSEA = 0.057 [0.055, 0.059], CFI = 0.897, TLI = 0.888). When this 7‐factor structure was loaded onto a single, second‐order factor of overall FCR, the model fit statistics were slightly poorer ( χ 2 = 3459.632, P < .0001, df = 807, RMSEA = 0.058 [0.056, 0.060], CFI = 0.893, TLI = 0.886). However, the difference between the models was significant (chi‐square difference = 103.142, P < .0001, df = 12) indicating that the first‐order model was a better fit to the data. Conclusions These results align with empirical and theoretical literature that supports the use of the FCRI as a multidimensional scale. Implications of results are discussed in light of FCR conceptualization and measurement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".