O015 Sleep, Stress, and Cognitive Fatigue in Canadian Wildland Firefighters
Bibliographic record
Abstract
Abstract Background Wildland firefighting requires continuous attention while exposed to long working hours and sub-optimal sleep. These stressors may induce a state of cognitive fatigue, which poses a risk to worker safety due to impaired judgment and reduced awareness. Methods The current study employs a within-subject, observational design to examine the effect of sleep and schedule characteristics on variables related to stress and cognitive function. Cognitive function is measured subjectively, via validated 7-point scales (i.e. Samn-Perelli Fatigue Scale and Stanford Sleepiness Scale), and objectively, via reaction time on the psychomotor vigilance task. Sleep is measured via sleep log and wrist-worn actigraphy. Stress via autonomic nervous system imbalance is measured by changes in heart rate variability (i.e. chest monitor). Linear regression analyses will be performed to test if sleep or schedule variables predict stress or cognitive function. Progress to date A pilot study (N=4) was conducted during the 2021 fire season to inform methodology. As of July 2022, a representative sample of 50 firefighters (13 F) have been recruited across all regions of British Columbia (BC), Canada. Data collection for the current study is ongoing and will continue until September 2022. Intended outcome and impact This investigation entails both theoretical and applied benefits. Its direct relevance to occupational health and safety could improve worker safety (1) by providing insight and recommendations towards improved fatigue management policy within the BC Wildfire Service and (2) by testing the practicality of mobile tools designed to monitor levels of sleep, stress, and cognitive function.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".