Subjective And Objective Cognitive Fatigue In Wildland Firefighting
Bibliographic record
Abstract
Wildland firefighting requires continuous attention while exposed to long working hours and sub-optimal sleep. These stressors may induce cognitive fatigue, which poses a risk to worker safety due to reduced awareness. PURPOSE: This pilot investigation examined the effect of sleep and shift characteristics on variables related to cognitive fatigue. METHODS: A within-subject, observational study was conducted on 4 wildland firefighters (3 M, 1F) between July and August of the 2021 fire season. Cognitive fatigue was measured before and after shift, both subjectively, via 7-point scales (Samn-Perelli Fatigue Scale (SPS); Stanford Sleepiness Scale (SSS)), and objectively, via mean reaction time (RT) on the 3-minute psychomotor vigilance task. Sleep variables were measured subjectively via sleep log and objectively via wrist-worn actigraphy. Linear regression analyses were performed to test if shift or sleep variables predicted cognitive fatigue variables. RESULTS: All sleep and shift variables significantly predicted subjective fatigue and sleepiness. SPS was most significantly predicted by shift duration (R2 = .52, F(1, 49) = 52.75, p < .001) and subjective sleep quality (R2 = .24, F(1, 105) = 32.59, p < .001). SSS was most significantly predicted by subjective sleep quality (R2 = .32, F(1, 105) = 49.7, p < .001) and start time (R2 = .21, F(1, 87) = 23.75, p < .001). Mean RT was not significantly predicted by any sleep or shift variable. However, RT was significantly predicted by both subjective sleepiness (R2 = .34, F(1, 55) = 28.52, p < .001) and fatigue (R2 = .12, F(1, 55) = 7.73, p < .01). Mean RT was also non-significantly predicted by objective sleep quality (R2 = .10 F(1, 25) = 2.83, p = 0.105). CONCLUSIONS: Subjective cognitive fatigue measures were significantly predicted by sleep and shift characteristics. Neither sleep nor shift variables significantly predicted mean RT directly, however, RT was significantly predicted by subjective fatigue and sleepiness.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".