Fatigue in wildland firefighting: relationships between sleep, shift characteristics, and cognitive function
Bibliographic record
Abstract
Background Wildland firefighting requires exposure to long shifts and poor sleep, which may pose a risk to worker safety due to impaired cognitive function. Aims We investigated the associations between sleep, shift characteristics, and cognitive function in wildland firefighters. Methods We conducted a within-subject observational study with 25 wildland firefighters from the British Columbia Wildfire Service, Canada. Data were collected remotely during the 2021 and 2022 fire seasons. Wrist-worn actigraphy and the psychomotor vigilance task served as objective, mobile measures of sleep and cognitive function, respectively. Web-based surveys were used to collect shift information and subjective cognitive function. Linear mixed effects modeling was used to control for inter-individual differences and explore the influence of participant-factors. Key results Average sleep duration on fire suppression days was 6.7 h (s.d. 66 min), while average shift duration was 13.8 h (s.d. 108 min). Poor sleep and longer shift durations were both associated with reduced cognitive function across all metrics (P < 0.01; P < 0.001). Conclusion Firefighters are often exposed to poor sleep and long shifts, which are both associated with impaired cognitive function. Implications Our results highlight the need for fire agencies to consider fatigue-related cognitive impairment as an important factor for worker health and safety.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".