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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".