RESUMEN

BACKGROUND: Implementation science presents ethical issues not well addressed by traditional research ethics frameworks. There is little empirical work examining how clinicians whose work is affected by implementation studies view these issues. Accordingly, we interviewed clinicians working at sites participating in an implementation study seeking to improve patient handoffs to the intensive care unit (ICU). METHODS: We performed semi-structured interviews with 32 clinicians working at sites participating in an implementation study aiming to improve patient handoffs from the operating room to the ICU. We analyzed the interviews using an iterative coding process following a conventional content analysis approach. RESULTS: Clinicians' greatest concern about involvement was possible damage to interpersonal relations with more senior clinicians. They were divided about whether informed consent from clinicians was necessary but were satisfied with the study's approach of sending out mass communications about the study. They did not think opting out of the implementation portion of the study was feasible but saw this inability to opt out as unproblematic because they equated the study with routine quality improvement. Those clinicians who helped launch the study at their sites recounted several different ways of doing so beyond simply facilitating access. CONCLUSIONS: The risks that clinicians identified stemmed more from their general status as employees than their specific work as clinicians. Implementation researchers should be attuned to the ethical ramifications of involving employees of varying ranks. Implementation researchers using hybrid designs should also be sensitive to the possibility that practitioners affected by a study will equate it with quality improvement and overlook its research component. Finally, the interactions that go into facilitating an implementation study are more various than the "gatekeeping" typically discussed by research ethicists. More research is needed on the ethics of the myriad interactions that are involved in making implementation studies happen.

RESUMEN

Resilient system performance in high-stakes settings, which includes the ability to monitor, respond, anticipate, and learn, can be enhanced for trainees through simulation of realistic scenarios enhanced by augmented reality. Active learning strategies can enhance simulation-based training, particularly the mental model articulation principle where students are prompted to anticipate what will happen next and the reflection principle where students self-assess their performance compared to a gold standard expert model. In this paper, we compared simulation-based training for trauma care with and without active learning strategies during pauses in the simulated action for progressively deteriorating patients. The training was conducted online and real-time without a facilitator, with 42 medical students viewing training materials and then immediately taking an online quiz for three types of trauma cases: hemorrhage, airway obstruction, and tension pneumothorax. Participants were randomly assigned to either the experimental or control condition in a between-subjects design. We compared performance in the control and experimental conditions based on: A) the proportion of cues correctly recognized, B) the proportion of accurate diagnoses, C) the proportion of appropriate treatment interventions, and D) verbal briefing quality on a 1-5 scale. We found that the training intervention increased recognition of subtle cues critical for accurate diagnosis and appropriate treatment interventions; the training did not improve the accuracy of diagnoses or the quality of the verbal briefing. We conclude that incorporating active learning strategies in simulation-based training improved foundational capabilities in detecting subtle cues and intervening to rescue deteriorating patients that can increase the readiness for trainees to contribute to resilient system performance in the high-stakes setting of emergency care in hospitals.

Asunto(s)

Competencia Clínica , Entrenamiento Simulado , Humanos , Masculino , Femenino , Entrenamiento Simulado/métodos , Estudiantes de Medicina/psicología , Aprendizaje Basado en Problemas/métodos , Adulto , Adulto Joven , Modelos Psicológicos , Realidad Virtual , Señales (Psicología) , Autoevaluación (Psicología) , Obstrucción de las Vías Aéreas , Hemorragia/terapia , Simulación de Paciente

RESUMEN

Apportionment in election systems refers to determination of the number of voting resources (poll books, poll workers, or voting machines) needed to ensure that all voters can expect to wait no longer than an appropriate amount, even the voter who waits the longest. Apportionment is a common problem for election officials and legislatures. A related problem is "allocation," which relates to the deployment of an existing number of resources so that the longest expected wait is held to an appropritate amount. Provisioning and allocation are difficult because the numbers of expected voters, the ballot lengths, and the education levels of voters may all differ significantly from precinct-to-precinct in a county. Consider that predicting the waiting time of the voter who waits the longest generally requires discrete event simulation.•The methods here rigorously guarantee that all voters expect to wait a prescribed time with a bounded probability, e.g., everyone expects to wait less than thirty minutes with probability greater than 95%.•The methods here can handle both a single type of resource (e.g., voting machines or scan machines) and multiple resource types (e.g., voting machines and poll books).•The methods are provided in a freely available, easy-to-use Excel software program.