The sleep behaviour and fatigue trends of wildland firefighters during non-fire and fire deployments
Bibliographic record
Abstract
Ontario wildland firefighting is a hazardous and safety-critical operation with relatively high injury rates. This is indicated by the 10-year average of 4.46 lost-time injuries per 100 workers in Ontario wildland firefighting compared to 0.95-1.88 lost-time injuries in other occupations, as reported by the Workplace Safety and Insurance Board (WSIB). There is anecdotal evidence that fatigue is a major contributor to injury; however, evidence to support this is limited. Understanding fatigue trends, potential causes, and areas for intervention within the wildland firefighting profession were the main goals of the study. Accordingly, contributors to fatigue were assessed during non-fire and fire deployments by collecting objective sleep (Actigraphy) and vigilance (Psychomotor Vigilance Test) measures, as well as subjective measures of fatigue and recovery (questionnaires). Data were collected from wildland firefighters during the high-risk months of the fire season within the province of Ontario. Sleep duration less than six hours, sleep efficiency below 85%, and wake after sleep onset greater than 30 min were more frequently observed during high intensity, Initial Attack deployments. Sleep duration less than six hours were routinely observed in non-fire work periods, placing workers at risk of pre-deployment sleep-debt. Self-reported morning fatigue scores were low-to-moderate and were best predicted by Initial Attack deployment work conducted the day prior. Reaction times were slightly worse in morning periods during Initial Attack deployments, but scores were generally within acceptable ranges. Self-reported recovery scores were generally good regardless of work performed. The current study highlights suboptimal sleep behaviours during both non-fire and fire suppression work, with sleep measures below recommended standards. High-intensity fire suppression periods (i.e. Initial Attacks) were predictably associated with the worst sleep and fatigue levels; but it is worth noting that the data were collected during a record low-hazard, firefighting season and may not reflect the sleep behaviours and fatigue levels encountered during a high-hazard fire season. Interventions for sleep/fatigue hygiene and awareness should be explored and are further discussed in this paper. The research methodology employed in the current study could be used in future investigations to determine the sleep behaviours and fatigue levels of wildland firefighters during a high hazard fire season.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".