Crowdsourced methods of data collection such as Amazon Mechanical Turk (MTurk) have been widely adopted in addiction science. Recent reports suggest an increase in poor quality data on MTurk, posing a challenge to the validity of findings. However, empirical investigations of data quality in addiction-related samples are lacking. In this study of individuals with alcohol use disorder (AUD), we compared poor quality delay discounting data to randomly generated data. A reanalysis of prior published delay discounting data was conducted comparing included, excluded, and randomly generated data samples. Nonsystematic criteria were implemented as a measure of data quality. The excluded data was statistically different from the included sample but did not differ from randomly generated data on multiple metrics. Moreover, a response bias was identified in the excluded data. This study provides empirical evidence that poor quality delay discounting data in an AUD sample is not statistically different from randomly generated data, suggesting data quality concerns on MTurk persist in addiction samples. These findings support the use of rigorous methods of a priori defined criteria to remove poor quality data post hoc. Additionally, it highlights that the use of nonsystematic delay discounting criteria to remove poor quality data is rigorous and not simply a way of removing data that does not conform to an expected theoretical model. Public Health SignificanceThis study provides empirical evidence that poor quality delay discounting data does not differ from random responding in a sample of individuals with alcohol use disorder. This highlights that previous reports of poor quality data on Amazon Mechanical Turk extend to addiction-related samples. Thus, the use of rigorous data quality controls and exclusion of poor quality data are warranted to ensure highquality scientific findings.

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Background. The amalgam of noises inherent to the modern-day operating room has the potential of diluting surgeon concentration, which could affect surgeon performance and mood and have implications on quality of care and surgeon resilience. Objective. Evaluate the impact of operating room environmental noises on surgeon performance including fine motor dexterity, cognition, and mood. Methods. 37 subjects were tested under three different environmental noise conditions including silence, a prerecorded soundtrack of a loud bustling operating room, and with background music of their choosing. We used the Motor Performance Series to test motor dexterity, neuropsychological tests to evaluate cognitive thinking, and Profile of Mood States to test mental well-being. Results. Our results showed that typical operating room noise had no impact on motor dexterity but music improved the speed and precision of movements and information processing skills. Neurocognitive testing showed a significant decrement from operating room noise on verbal learning and delayed memory, whereas music improved complex attention and mental flexibility. The Profile of Mood States found that music resulted in a significant decrease in feelings of anger, confusion, fatigue, and tension along with decreased total mood disturbance, which is a measure of psychological distress. Loud operating room noise had a negative impact on feelings of vigor but no increase in total mood disturbance. Conclusion. Our results suggest that loud and unnecessary environmental noises can be distracting to a surgeon, so every effort should be taken to minimize these. Music of the surgeons’ choosing does not negatively affect fine motor dexterity or cognition and has an overall positive impact on mood and can therefore be safely practiced if desired.