DESIGN: A cross sectional study.
METHOD: A self-administered online survey was used from August to October 2022, with a sample size of 417 nursing students selected through convenience sampling. Descriptive statistics, correlation analyses, and PROCESS macro v4.1 (Model 4) were used for data analysis.
RESULTS: The results revealed that virtual learning infrastructure, access to electronic facilities, and student collaboration, significantly predict student computer competency and e-learning outcomes. Virtual learning infrastructure and access to electronic facilities were found to be the strongest predictors of student computer competency, while student collaboration had a smaller but still significant effect. Student computer competency was found to mediate the relationship between virtual learning infrastructure, access to electronic facilities, student collaboration, and e-learning outcomes.
OBJECTIVE: From the considerable amount of clinical narrative text, natural language processing (NLP) researchers have developed methods for extracting ADEs and their related attributes. This work presents a systematic review of current methods.
METHODOLOGY: Two biomedical databases have been searched from June 2022 until December 2023 for relevant publications regarding this review, namely the databases PubMed and Medline. Similarly, we searched the multi-disciplinary databases IEEE Xplore, Scopus, ScienceDirect, and the ACL Anthology. We adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement guidelines and recommendations for reporting systematic reviews in conducting this review. Initially, we obtained 5,537 articles from the search results from the various databases between 2015 and 2023. Based on predefined inclusion and exclusion criteria for article selection, 100 publications have undergone full-text review, of which we consider 82 for our analysis.
RESULTS: We determined the general pattern for extracting ADEs from clinical notes, with named entity recognition (NER) and relation extraction (RE) being the dual tasks considered. Researchers that tackled both NER and RE simultaneously have approached ADE extraction as a "pipeline extraction" problem (n = 22), as a "joint task extraction" problem (n = 7), and as a "multi-task learning" problem (n = 6), while others have tackled only NER (n = 27) or RE (n = 20). We further grouped the reviews based on the approaches for data extraction, namely rule-based (n = 8), machine learning (n = 11), deep learning (n = 32), comparison of two or more approaches (n = 11), hybrid (n = 12) and large language models (n = 8). The most used datasets are MADE 1.0, TAC 2017 and n2c2 2018.
CONCLUSION: Extracting ADEs is crucial, especially for pharmacovigilance studies and patient medications. This survey showcases advances in ADE extraction research, approaches, datasets, and state-of-the-art performance in them. Challenges and future research directions are highlighted. We hope this review will guide researchers in gaining background knowledge and developing more innovative ways to address the challenges.
MATERIALS AND METHODS: This quality improvement project was undertaken at a private university. Guided by the PDSA model, rubber dam application tasks were conducted in the simulation lab in 2 phases. Phase 1 included TBSL and phase 2 included EBSL comprising of 2 PDSA cycles. 'Plan' stage involved obtaining feedback from students and the concerned staff. 'Do' stage included implementation of EBSL in eight steps adopted from Higgins's framework. 'Study' stage evaluated the outcomes and in 'Act' stage amendments were made to the first EBSL cycle. In the second PDSA cycle re-implementation and evaluation of the rubber dam application exercises were carried out. Descriptive data were presented as percentages and mean scores were compared using paired t-test.
RESULTS: Thirty-seven year 2 students participated in this study. A significant improvement in the mean scores was observed between TBSL and EBSL (3.02 + 0.16 and 3.91 + 0.27, respectively, p
EDUCATIONAL ACTIVITY AND SETTING: A cross-sectional online survey involving second-year pharmacy students of Monash Malaysia (MA) and Monash Australia (PA) campuses was conducted. The survey consisted of 15 Likert-scale multiple-choice questions and an open-ended question. Data were analysed statistically.
FINDINGS: Students at both MA and PA campuses were satisfied with the remote online learning experienced during the pandemic but indicated a preference for a blended learning approach. Students at the MA campus felt that on-campus face-to-face classes were more engaging and advantageous for their learning and skills development (P
EDUCATIONAL ACTIVITY AND SETTING: A one day accelerated dispensing course using MyDispense software was delivered to 59 GE students. The accelerated dispensing course was identical to the standard three-week dispensing course delivered to UG students. The same assessment of dispensing skills was conducted after course completion for both UG and GE students and included dispensing four prescriptions of varying difficulty. The assessment scores of the UG and GE students were compared. Perception data from the accelerated course were also collected.
FINDINGS: The accelerated dispensing curriculum was well received by students. They found the simulation relevant to practice, easy to navigate, and helpful for preparing them for assessment. Overall, 5.1% of GE students failed the assessment, which was lower than the 32.6% failure rate in the UG cohort. Comparison of assessment grades between UG and GE students showed no notable disadvantage to attainment of learning outcomes with the accelerated curriculum. However, UG students were more likely to provide unsafe instructions compared to GE students in their labeling for three out of four prescriptions.
SUMMARY: An accelerated dispensing curriculum can be effectively delivered to mature learners with a prior science-related degree as no notable deficiencies were identified when comparing the assessment results of GE students against UG students when both student cohorts undertook the same dispensing assessment.