Pharmacy Student Perceptions of Volunteering at a Medication Assessment Clinic Located Within a Pharmacy School

Context In 2011 the College of Pharmacy and Nutrition (University of Saskatchewan) opened a patient care clinic on campus known as the Medication Assessment Centre (MAC). The primary purpose of the MAC is to offer a faculty supervised experiential training opportunity for pharmacy students in all years of study. The early experiential education model that the MAC utilizes had not been previously evaluated in the literature. Objective The purpose of this study was to explore the experiences of MAC student volunteers. Design The perspectives of students who had volunteered at the MAC at least once between January and November 2015 were gathered through focus groups. Students were assigned to one of five focus groups based on their volunteer title and number of MAC volunteer experiences. A semi-structured focus group guide was developed and used to gather the students’ perceptions on their experiences and learning as a result of volunteering at the MAC. The focus groups were recorded and transcribed. The transcripts were analyzed by three researchers using thematic analysis. The final themes were approved by the student participants and then reviewed by an additional researcher. Results A total of 29 students participated in this study. Students perceived that the MAC had a positive effect on their learning and competence in the following areas: (1) clinical skills (patient interviewing and communication), (2) confidence, (3) clinical and therapeutic knowledge, and (4) professional socialization. Students felt the post discussion, patient care environment and actively participating were most beneficial to their learning. The aspects of the MAC that students liked most were: (1) structure of the learning experience, (2) perceived benefit to the patient, and (3) patient care environment. Students identified several challenges to participating: (1) sign up process, (2) quality of the technology, (3) remote observation, (4) limited student knowledge, (5) clarity of student role, and (6) student initial confidence. Conclusions MAC student volunteers felt that the MAC is a valuable learning experience that had a positive effect on their learning and competence. Further research should focus on confirming these findings in a larger sample and using additional methodologies such as quantitative assessments of student learning and competency

HapticDive: An Intuitive Warning System for Underwater Users

All divers—regardless of skill or activity—are constantly at risk of decompression sickness; mild symptoms can often go ignored, and can also be deadly if left untreated. Currently, divers receive training and carry a dive computer or a combination of a depth gauge and a depth watch for checking to avoid such situations. However, this equipment does not warn a user if they are in danger of decompression sickness, since users have to keep track of their ascension rates and since shallow-water divers often carry minimal equipment. This work proposes an application called HapticDive to keep track of a user’s depth in relation to the time passed underwater. The application paces their ascent to the surface by providing “stop” signals to users as an audio-visual combination, so that users avoid experiencing “the bends” (i.e., decompression sickness symptoms). HapticDive aims to provide the foundation for a cost-effective application that warns divers— especially surface supported divers, free divers, and general shallow-water divers—when they are at risk of decompression sickness, so they may avoid symptom

AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i.e. text, image, video, audio, IMU motion sensor), and generates textual responses. AnyMAL inherits the powerful text-based reasoning abilities of the state-of-the-art LLMs including LLaMA-2 (70B), and converts modality-specific signals to the joint textual space through a pre-trained aligner module. To further strengthen the multimodal LLM's capabilities, we fine-tune the model with a multimodal instruction set manually collected to cover diverse topics and tasks beyond simple QAs. We conduct comprehensive empirical analysis comprising both human and automatic evaluations, and demonstrate state-of-the-art performance on various multimodal tasks

The Montana Kaimin, November 13, 1931

Student newspaper of the University of Montana, Missoula.https://scholarworks.umt.edu/studentnewspaper/2318/thumbnail.jp