Accident; analysis and prevention
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To operate Navy ships 24h per day, watchstanding is needed around the clock, with watch periods reflecting a variety of rotating or fixed shift schedules. The 5/15 watch schedule cycles through watch periods with 5h on, 15h off watch, such that watches occur 4h earlier on the clock each day - that is, the watches rotate backward. The timing of sleep varies over 4-day cycles, and sleep is split on some days to accommodate nighttime watchstanding. ⋯ These laboratory-based findings suggest that Navy watch schedules are associated with cumulative sleep loss and a build-up of fatigue across days. The fixed watch periods of the 3/9 watch schedule appear to yield more stable performance than the backward rotating watch periods of the 5/15 watch schedule. Optimal performance may require longer and more consistent daily opportunities for sleep than are typically obtained in Navy operations.
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Sleep-related (SR) crashes are an endemic problem the world over. However, police officers report difficulties in identifying sleepiness as a crash contributing factor. One approach to improving the sensitivity of SR crash identification is by applying a proxy definition post hoc to crash reports. ⋯ When interpreting crash data it is important to understand the implications of SR identification because strategies aimed at reducing the road toll are informed by such data. Without the correct interpretation, funding could be misdirected. Improving sleepiness identification should be a priority in terms of both improvement to police and proxy reporting.
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Night shift workers are at risk of road accidents due to sleepiness on the commute home. A brief nap at the end of the night shift, before the commute, may serve as a sleepiness countermeasure. However, there is potential for sleep inertia, i.e. transient impairment immediately after awakening from the nap. ⋯ SP-Fatigue and driving performance did not differ significantly between conditions. In conclusion, the pre-drive nap showed objective, but not subjective, evidence of sleep inertia immediately after awakening. The 10-min nap did not affect driving performance during the simulated commute home, and was not effective as a sleepiness countermeasure.
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In the military or emergency services, operational requirements and/or community expectations often preclude formal prescriptive working time arrangements as a practical means of reducing fatigue-related risk. In these environments, workers sometimes employ adaptive or protective behaviours informally to reduce the risk (i.e. likelihood or consequence) associated with a fatigue-related error. These informal behaviours enable employees to reduce risk while continuing to work while fatigued. ⋯ Rather, they have evolved spontaneously as part of the culture around protecting team performance under adverse operating conditions. When compared with previous similar studies, aviation personnel were more readily able to understand the idea of fatigue proofing than those from a fire-fighting background. These differences were thought to reflect different cultural attitudes toward error and formal training using principles of Crew Resource Management and Threat and Error Management.
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Injury narratives are now available real time and include useful information for injury surveillance and prevention. However, manual classification of the cause or events leading to injury found in large batches of narratives, such as workers compensation claims databases, can be prohibitive. In this study we compare the utility of four machine learning algorithms (Naïve Bayes, Single word and Bi-gram models, Support Vector Machine and Logistic Regression) for classifying narratives into Bureau of Labor Statistics Occupational Injury and Illness event leading to injury classifications for a large workers compensation database. ⋯ Integrating LR into this ensemble mix improved performance only slightly. For large administrative datasets we propose incorporation of methods based on human-machine pairings such as we have done here, utilizing readily-available off-the-shelf machine learning techniques and resulting in only a fraction of narratives that require manual review. Human-machine ensemble methods are likely to improve performance over total manual coding.