Using a biomathematical model to assess fatigue risk and scheduling characteristics in Canadian wildland firefighters
Bibliographic record
Abstract
This study examined the shift parameters that contribute to sleep loss and on-duty fatigue in British Columbia Wildfire Service (BCWS) firefighters using sleep–wake data, work–rest data and alertness and fatigue predictions from a biomathematical model (BMM) of fatigue. A total of 40 firefighters (age: 30.4 ± 11.6 years; 13 F, 26 M) volunteered over a 14-day consecutive fireline deployment, followed by a 3-day rest period, at two separate fires in British Columbia (during the 2015 fire season). Sleep–wake data were obtained using a wrist-worn accelerometer and self-reported sleep logs. Shift start and end times were provided by the BCWS at the completion of the study. Sleep and shift data were manually entered into a validated BMM (Circadian Alertness Simulator) to generate fatigue scores and shift work patterns. Shift duration was the major contributor to fatigue, as 46% (n = 274) of shifts were ≥14 h in length and the average shift length was 13.0 ± 0.62 h. However, none of the firefighters had a high-risk fatigue score (>60). The findings from this study indicated that using a BMM of fatigue can provide important insights into shift-work parameters that contribute to workplace fatigue and sleep loss in wildland firefighters.
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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 | 0.000 | 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".