Development and Validation of Fear of Relapse Scale for Relapsing-Remitting Multiple Sclerosis
Bibliographic record
Abstract
Background: Multiple Sclerosis (MS) is a potentially debilitating chronic disease in most cases diagnosed after an acute relapse and characterized by the occurrence of relapse in most patients. Due to the unknown course of the disease patients in early phases must deal with the stress of anticipation of a relapse and unpredictable consequences of that relapse. Objective: This is the first effort to develop a self-report measure of Fear of Relapse (FoR) in patients with Relapsing-Remitting (RR) MS. Methods: A 31- item scale was created from in-depth clinical interviews with 33 RRMS patients. This scale was completed by 168 RRMS patients (51 patients completed the scale one more time a month later) who completed the Intolerance of Uncertainty Scale (IUS) and Depression, Anxiety and Stress Scale (DASS) as well. Results: A factor analysis revealed three components, and five items failed to load on any of them. The final version of the scale consisted of 26 items. Two-components solution factor analysis after pooling the FoR items once with DASS items and once with IUS items revealed independency of the FoR from previously developed scales. Cronbach’s Alpha was equal to 0.92. Test-retest reliability for total score was equal to 0.74 (p<0.001). Conclusion: The FoR scale proved to be a highly reliable and valid measure in RRMS patients and application of that in future studies trying to create a psychological profile of patients at earlier stages of the disease can help researchers and clinicians to have a more comprehensive image.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".