Overview of Self-Reported Measures of Fatigue
Why this work is in the frame
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Bibliographic record
Abstract
Objective: The purpose of this article is to provide a quick summary of existing measures with reliability and validity data to help researchers select a subjective measure appropriate for their application.Background: Currently, fatigue is measured through self-rating (asking individuals if they are experiencing fatigue, tiredness, or sleepiness), and calculation of fatigue from self-reported sleep and work patterns.Method: Self-rated measures of fatigue are summarized.Results: Extant fatigue scales include the Brief Fatigue Inventory, Chalder Fatigue Scale, Checklist Individual Strength, Chronic Fatigue Scale, Crew Status Survey (also known as the Samn–Perelli Fatigue Scale), Daytime Sleepiness Scale, Epworth Sleepiness Scale, Fatigue, Anergy, Consciousness, Energized and Sleepiness, Fatigue Assessment Inventory, Fatigue Assessment Scale, Fatigue Impact Scale, Fatigue Severity Scale, Fatigue Symptom Inventory, Functional Assessment of Cancer Therapy, Karolinska Sleepiness Scale, Maslach Burnout Inventory Emotional Exhaustion Subscale, Modified Brief Fatigue Inventory, Multidimensional Fatigue Inventory, Patient-Reported Outcomes Measurement Information System (PROMIS) Short Form Fatigue Questionnaire, Piper Fatigue Scale, Sleep Wake Activity Inventory, Samm–Perelli Seven-Point Fatigue Scale (SPS), Stanford Sleepiness Scale, Visual Analog Fatigue Scale, and World Health Organization Quality Of Life Assessment Energy and Fatigue subscale. In addition to the self-rating of fatigue scales, several measures are calculated and predicted from self-reported amount and quality of sleep as well as work schedule. These biomathematical models include the Fatigue Avoidance Scheduling Tool (FAST), Fatigue Audit InterDyne, Fatigue Index Tool (FIT), and the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) Model. Note that the FAST uses the SAFTE model and the combination is sometimes referred to as SAFTE/FAST (Hursh, 2003). These models are also summarized in this article.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it