The development and validation of the Unidimensional Fatigue Impact Scale (U-FIS)
Bibliographic record
Abstract
BACKGROUND: The multidimensional assessment of fatigue is complicated by the interrelation of its multiple causes and effects. OBJECTIVE: The purpose of the research was to develop a unidimensional assessment of fatigue (U-FIS). METHODS: Data collected with the Fatigue Impact Scale (FIS) were subjected to Rasch analysis to identify potential problems with the scale. Additional items for the U-FIS were generated from interviews with UK MS patients. The U-FIS was tested for face and content validity in patient interviews and included in a validation survey to determine dimensionality (Rasch model), reliability and validity. RESULTS: The original FIS was not unidimensional when subscale items were combined. The modification of the FIS and addition of a number of items allowed the development of a 22-item unidimensional scale (U-FIS) that was reliable (Cronbach Alpha = 0.96; test-retest = 0.86,) and valid given correlations with the Nottingham Health Profile and ability to distinguish between MS severity groups. There was no significant difference in U-FIS scores according to MS type. CONCLUSION: It is valid to conceptualize the functional impact of fatigue as unidimensional. The U-FIS is a reliable and valid questionnaire that will allow the measurement of this construct in clinical studies.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".