Sleep Disorders, Health, and Safety in Police Officers
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».