Impaired Sleep In Volunteer Firefighters Responding To Nighttime Calls
Bibliographic record
Abstract
In Canada and the US, there are approximately 813,000 volunteer firefighters (FFs), a unique subset of shift workers who, despite possessing separate careers, provide 24-hour emergency services. Despite accounting for 67% of North American FFs, volunteers are often overlooked in firefighter research in favour of their career counterparts. It is known that calls at night reduce sleep and that sleep deprivation can adversely affect executive function however, the degree of sleep deprivation among on-call volunteer FFs remains a paucity. PURPOSE: To quantify the impact of volunteer FFs’ night time call response on sleep volume and stage-specific distribution. METHODS: Eight male volunteer FFs (34.76 ± 2.56 years) wore validated wristband sleep monitors to track total, stage-specific, and percent distribution of sleep on nights without a call (CON), and on nights where there was a call response between 1900 and 0700 (CALL). Data was extracted via the device’s app to a tablet and recorded via spreadsheet. One firefighter experienced two nights with a call and only one without. Both sets of CALL data were compared to the CON resulting in 9 sets of CON:CALL data which were analyzed using a one-way ANOVA. RESULTS: Significant differences were found in total sleep (CON: 417.125 ± 52.044 mins; CALL: 261.111 ± 61.116 mins), time spent in rapid-eye movement (REM) (CON: 109.88 ± 28.47 mins; CALL: 51.44 ± 17.92 mins) and light sleep (CON:225.75 ± 26.20 mins; CALL: 157.89 ± 37.54 mins), and percentage of sleep spent in REM (CON: 22.25 ± 3.73%; CALL: 16.44 ± 3.17%). This was accompanied by respective effect sizes (η2) of .570, .537, .429, and .511. Despite comprising 22.57% of total CON sleep, REM sleep decreased disproportionately, accounting for 37% of CALL sleep loss. CONCLUSIONS: Volunteer firefighters responding to overnight calls experience significant total sleep deprivation at levels previously shown to impede cognitive performance. Significant and disproportionate decreases in total and percentage of REM sleep were also observed on nights with a call. Considering the impact of REM sleep on optimal executive function, this degree of sleep deprivation has the ability to impact critical decision-making events, not only on the fire ground, but at the firefighter’s day job, thereby increasing risk of injury/death.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".