Sleep Disorders, Health, and Safety in Police Officers
Bibliographic record
Abstract
CONTEXT: Sleep disorders often remain undiagnosed. Untreated sleep disorders among police officers may adversely affect their health and safety and pose a risk to the public. OBJECTIVE: To quantify associations between sleep disorder risk and self-reported health, safety, and performance outcomes in police officers. DESIGN, SETTING, AND PARTICIPANTS: Cross-sectional and prospective cohort study of North American police officers participating in either an online or an on-site screening (n=4957) and monthly follow-up surveys (n=3545 officers representing 15,735 person-months) between July 2005 and December 2007. A total of 3693 officers in the United States and Canada participated in the online screening survey, and 1264 officers from a municipal police department and a state police department participated in the on-site survey. MAIN OUTCOME MEASURES: Comorbid health conditions (cross-sectional); performance and safety outcomes (prospective). RESULTS: Of the 4957 participants, 40.4% screened positive for at least 1 sleep disorder, most of whom had not been diagnosed previously. Of the total cohort, 1666 (33.6%) screened positive for obstructive sleep apnea, 281 (6.5%) for moderate to severe insomnia, 269 (5.4%) for shift work disorder (14.5% of those who worked the night shift). Of the 4608 participants who completed the sleepiness scale, 1312 (28.5%) reported excessive sleepiness. Of the total cohort, 1294 (26.1%) reported falling asleep while driving at least 1 time a month. Respondents who screened positive for obstructive sleep apnea or any sleep disorder had an increased prevalence of reported physical and mental health conditions, including diabetes, depression, and cardiovascular disease. An analysis of up to 2 years of monthly follow-up surveys showed that those respondents who screened positive for a sleep disorder vs those who did not had a higher rate of reporting that they had made a serious administrative error (17.9% vs 12.7%; adjusted odds ratio [OR], 1.43 [95% CI, 1.23-1.67]); of falling asleep while driving (14.4% vs 9.2%; adjusted OR, 1.51 [95% CI, 1.20-1.90]); of making an error or safety violation attributed to fatigue (23.7% vs 15.5%; adjusted OR, 1.63 [95% CI, 1.43-1.85]); and of exhibiting other adverse work-related outcomes including uncontrolled anger toward suspects (34.1% vs 28.5%; adjusted OR, 1.25 [95% CI, 1.09-1.43]), absenteeism (26.0% vs 20.9%; adjusted OR, 1.23 [95% CI, 1.08-1.40]), and falling asleep during meetings (14.1% vs 7.0%; adjusted OR, 1.95 [95% CI, 1.52-2.52]). CONCLUSION: Among a group of North American police officers, sleep disorders were common and were significantly associated with increased risk of self-reported adverse health, performance, and safety outcomes.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".