[Learning from Mistakes? - A Randomized Controlled Trial on the Acquisition of Knowledge about Clinical Communication Skills Using Positive vs. Mixed Therapy Models]. | LitMetric
Objective: This study compares two types of therapeutic model videos: an ideal model and a model that shows mistakes. The idea is that the conscious perception of mistakes is more likely to help build a comprehensive understanding of clinical communication skills than an ideal model.
Methods: A total of n1=111 psychology students and n2=57 people from the general population were randomly assigned to one of two training conditions as part of an online study. While one group watched a short but competent conversation of a behavioral therapist in their training with a positive model video, the other group watched a mixed model video in their training that showed a therapist with mediocre competence. In both training videos, the positive or negative behavior was marked with written explanations. Before and after the training, the participants rated the competencies of a therapist in another interview situation using standardized scales. These competence ratings were compared with those of two clinical experts and thus provided an indicator of the participants' conceptual knowledge of competent interviewing.
Results: A series of ANCOVA models showed that the group that saw the mixed model video deviated significantly less from the experts after training than the group that saw the positive model video (ηp2=0.03-0.10). However, the group that watched the positive (vs. mixed) model video deviated more strongly from the expert judgments on two of three competence scales after the training than before (Pre-Post=0.78-0.82).
Discussion: Overall, the hypothesis that mixed models are advantageous was confirmed. The unexpected results in the group with the positive model video could be explained by the fact that they set an unrealistically high anchor to which the later behavior is compared.
Conclusion: Mixed models may offer some advantage over positive models in imparting knowledge about professional communication, especially when the model videos contain behaviorally relevant explanations.
Accurate intra-operative Remaining Surgery Duration (RSD) predictions allow for anaesthetists to more accurately decide when to administer anaesthetic agents and drugs, as well as to notify hospital staff to send in the next patient. Therefore, RSD plays an important role in improved patient care and minimising surgical theatre costs via efficient scheduling. In endoscopic pituitary surgery, it is uniquely challenging due to variable workflow sequences with a selection of optional steps contributing to high variability in surgery duration.
Introduction: Latina, Latino, Latinx, Latine, Hispanic, or of Spanish origin+ (LHS+) women face higher cervical cancer risks, incidence, and mortality compared to non-Hispanic White women. These disparities are attributable to socioeconomic factors, limited access to health care, language and cultural barriers, and negative health care experiences.
Methods: We used the Kern model to design, implement, and evaluate a workshop to educate medical students and health care professionals on cervical cancer disparities among LHS+ women and culturally competent communication skills.
MolecularWebXR is a new web-based platform for education, science communication and scientific peer discussion in chemistry and biology, based on modern web-based Virtual Reality (VR) and Augmented Reality (AR). With no installs as it is all web-served, MolecularWebXR enables multiple users to simultaneously explore, communicate and discuss concepts about chemistry and biology in immersive 3D environments, by manipulating and passing around objects with their bare hands and pointing at different elements with natural hand gestures. Users may either be present in the same physical space or distributed around the world, in the latter case talking naturally with each other thanks to built-in audio.
Background: The development of Artificial Intelligence in the healthcare sector is generating a great impact. However, one of the primary challenges for the implementation of this technology is the access to high-quality data due to issues in data collection and regulatory constraints, for which synthetic data is an emerging alternative. While previous research has reviewed synthetic data generation techniques, there is limited focus on their applications and the motivations driving their synthesis.
Objective: Telemonitoring (TM) remotely monitors individuals' health. Awareness of personal clinical data has resulted in improved glycaemic control in adults with type-2 diabetes mellitus (T2DM). However, its effects on their health-seeking behaviour remain unclear.
