Objective: The aim of this study was to develop and psychometrically validate a new instrument that comprehensively measures video game satisfaction based on key factors. Background: Playtesting is often conducted in the video game industry to help game developers build better games by providing insight into the players’ attitudes and preferences. However, quality feedback is difficult to obtain from playtesting sessions without a quality gaming assessment tool. There is a need for a psychometrically validated and comprehensive gaming scale that is appropriate for playtesting and game evaluation purposes. Method: The process of developing and validating this new scale followed current best practices of scale development and validation. As a result, a mixed-method design that consisted of item pool generation, expert review, questionnaire pilot study, exploratory factor analysis ( N = 629), and confirmatory factor analysis ( N = 729) was implemented. Results: A new instrument measuring video game satisfaction, called the Game User Experience Satisfaction Scale (GUESS), with nine subscales emerged. The GUESS was demonstrated to have content validity, internal consistency, and convergent and discriminant validity. Conclusion: The GUESS was developed and validated based on the assessments of over 450 unique video game titles across many popular genres. Thus, it can be applied across many types of video games in the industry both as a way to assess what aspects of a game contribute to user satisfaction and as a tool to aid in debriefing users on their gaming experience. Application: The GUESS can be administered to evaluate user satisfaction of different types of video games by a variety of users.
The T-TPQ is a construct-valid instrument for measuring perceptions of teamwork. This has beneficial implications for patient safety and future research that studies medical teamwork.
Human error poses significant risk for hospitalized patients causing an estimated 100,000 to 400,000 deaths in the USA annually. Medication errors contribute, with error occurring in 5.3% of medication administrations during surgery. In this study 70.3% of medication errors were deemed preventable. Given the paucity of randomized controlled studies, we undertook a rigorous review of the literature to identify recommendations supported by expert opinions. An extensive literature search pertaining to medication error, medication safety, operating room, and anaesthesia was performed. The National Guidelines Clearinghouse was searched for any anaesthesia or operating room medication safety guidelines.A total of 74 articles were included. Recommendations were tabulated and assigned points based on a scale revised from a prior study. A total of 138 unique recommendations were identified, with point tallies ranging from 4 to 190. An in-person focus meeting occurred, where the 138 recommendations were reviewed, combined and condensed. A modified Delphi process was used to eliminate items found to be unimportant or those unable to be quantified (e.g. "minimize fatigue"). A total of 35 specific recommendations remained. Adverse events as a result of medication errors occur frequently in the operative setting. There are few rigorous studies to direct medication safety strategies, but this should not lead us to do nothing. The overwhelming consensus regarding best practices should be accepted, and the recommendations implemented. Our list of recommended strategies can hopefully be used to assess local vulnerabilities and institute system solutions.
Gone are the days of robots solely operating in isolation, without direct interaction with people. Rather, robots are increasingly being deployed in environments and roles that require complex social interaction with humans. The implementation of human-robot teams continues to increase as technology develops in tandem with the state of human-robot interaction (HRI) research. Trust, a major component of human interaction, is an important facet of HRI. However, the ideas of trust repair and trust violations are understudied in the HRI literature. Trust repair is the activity of rebuilding trust after one party breaks the trust of another. These trust breaks are referred to as trust violations . Just as with humans, trust violations with robots are inevitable; as a result, a clear understanding of the process of HRI trust repair must be developed in order to ensure that a human-robot team can continue to perform well after a trust violation. Previous research on human-automation trust and human-human trust can serve as starting places for exploring trust repair in HRI. Although existing models of human-automation and human-human trust are helpful, they do not account for some of the complexities of building and maintaining trust in unique relationships between humans and robots. The purpose of this article is to provide a foundation for exploring human-robot trust repair by drawing upon prior work in the human-robot, human-automation, and human-human trust literature, concluding with recommendations for advancing this body of work.
Significant effects were found for protocols across provider types, regardless of expertise or area of clinical focus. It also appears that more thorough protocols lead to more information being passed, especially when those protocols consist of 12 or more items. Given these findings, publication bias is an apparent feature of this literature base. Recommendations to reduce the apparent publication bias in the field include changing the way articles are screened and published.
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