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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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".