Changes in medical student learning preferences help drive innovation in teaching and require schools and commercial resources to quickly adapt. However, few studies have detailed the relationship of learner preferences to the environment and teaching modalities used in the pre-clerkship years, nor do they incorporate third-party resources. Our study attempts to analyze learner preferences by comparing the use of traditional and third-party resources. In 2017-18, a survey was distributed to medical students and residents at two accredited medical schools. Participants noted preferred styles of learning regarding lecture duration, timing, location, format, third-party resources, learner types and USMLE Step 1 scores. The 'Learning Environment, Learning Processes, and Learning Outcomes' (LEPO) framework [5] was used to examine learner preferences, with responses compared using the Mann-Whitney U and two proportion z-tests. A total of 329 respondents completed the survey: 62.7% medical students and 37.3% residents. The majority of participants identified their learning style by Kolb [6] as converging (33.0%) or accommodating (39.2%). Students preferred lectures 30-40 minutes long (43.3%), during morning hours (54.2%), in their own homes (52.0%), via online lectures with simultaneous drawings (56.0%), and classroom/ podcast lectures with PowerPoint® presentations (54.3%). Overall, students rated third-party resource characteristics higher than traditional curricula, including effectiveness of teachers, length, quality, time of day, and venue (p < 0.001), but also preferred small group formats. Students reported animated videos (46.6%) and simultaneous drawings (46.5%) as the most effective means of retaining information. Understanding changing learner preferences is important in creating optimal curricula for today's students. Using the LEPO framework, this study identifies critical preferences in successfully teaching medical students, inclusive of commercial and traditional resources. These results can also help guide changes in pedagogy necessary due to the more recent COVID-19 pandemic.
Violence against women is seen as sexual or physical activity committed against women. In India, general forms of violence against women in India includes cruelty by relatives, dowry, rape, sexual assault, kidnapping, immoral trafficking, molestation etc. The security of the women is the utmost priority of any government in this world. In India, many policies and laws have been enforced to ensure the safety against women. Technology is being the biggest supporter to the government in this context. Data mining allows various techniques such as clustering classification, regression provides analysis in any form of data and helps intelligent predictions on the given dataset. In this paper, we use k-means clustering analysis on women crime dataset. As a part of pre-processing, we collated the data entries which had crime cases against women and made women crime sub-dataset from the real dataset. We then applied K means clustering for further analysis. We used a rapid miner tool for clustering analysis as it is widely used for clustering purposes. After completion of clustering analysis, we proposed our views and discussions on the clustering results. At the end, we ended up giving the futuristic work to be further done on the derived dataset we made and made available on public repositories.
Study objectives To evaluate leg movements during sleep (LMS) in children taking serotonergic antidepressants, compared to those of children with restless legs syndrome (RLS) and controls, and to assess the time structure of intermovement intervals (IMI). Methods Twenty-three children (12 girls, mean age 14.1 years) on antidepressants and with a total LMS index ≥15/hour, 21 drug-naïve RLS children (11 girls, mean age 13.6 years) also with total LMS index ≥15/hour, and 35 control children (17 girls, mean age 14.3 years) were recruited. LMS were scored and a series of parameters was calculated, along with the analysis of their time structure. Results Children taking antidepressants showed higher total and periodic LMS (PLMS) indexes than both controls and RLS children, as well as higher short-interval and isolated LMS indexes than controls. LMS periodicity was highest in children on antidepressants. In children taking antidepressants, a well-defined PLMS IMI peak corresponding to ~10-60 s, with a maximum at ~20 s was present, which was much less evident in RLS patients and absent in controls. A progressive decrease of PLMS during the night and more frequent arousals were found in children on antidepressants and with RLS. Conclusions Children taking serotonergic antidepressants show higher periodicity LMS than children with RLS or controls and have a higher number of PLMS through the night. Antidepressant-associated PLMS in children seem to have features similar to PLMS of adults with RLS. Whether this is a marker of an increased risk to develop RLS later in life needs to be determined.
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