Jonassensharma6723
The coronavirus disease 2019 (COVID-19) pandemic is a global health crisis that has impacted daily life due to the policies created to contain the outbreak. Recent studies showed that medical students, a high-stress population, experienced deteriorated mental well-being during the pandemic. The aim of the present study was to assess stress and the need for support among Thai medical students during the COVID-19 pandemic, as a multicenter study.
The present study was a cross-sectional questionnaire-based study which collected data from second through sixth year medical students. Data was collected during the pandemic from multiple medical schools spanning all six regions of Thailand. Questionnaires included demographic data; the Thai version of the Perceived Stress Scale-10 (T-PSS-10) assessing stress level and the sources of stress; and the received supports from medical schools, the satisfaction with the supports, and the further necessary needs.
There were 1,395 medical students who responded to the questionnaires. Mean T-PSS-10 score was 17.8. Most of the sources of stress were related to the changing of teaching and evaluation system. Students residing in larger medical schools were significantly more satisfied with received support and tended to gain greater support than those in medium and small sized schools. Stress-relieving activities arrangement was considered the most sought after additional support by students.
Medical student stress levels were higher during the pandemic compared to pre-pandemic levels. Stress relieving activities, availability and access to mental health resources, and other strategies to reduce stress among medical students are urgently needed.
Medical student stress levels were higher during the pandemic compared to pre-pandemic levels. Stress relieving activities, availability and access to mental health resources, and other strategies to reduce stress among medical students are urgently needed.
This study aims to design that using formative assessment as an instructional strategy in real-time online classes, and to explore the application of Bloom's taxonomy in the development of formative assessment items.
We designed the instruction using formative assessment in real-time online classes, developed the items of formative assessment, analyzed the items statistically, and investigated students' perceptions of formative assessment through a survey.
It is designed to consist of 2-3 learning outcomes per hour of class and to conduct the formative assessment with 1-2 items after the lecture for each learning outcome. Formative assessment was 31 times in the physiology classes (total 48 hours) of three basic medicine integrated. There were nine "knowledge" items, 40 "comprehension" items, and 55 "application" items. There were 33 items (31.7%) with a correct rate of 80% or higher, which the instructor thought was appropriate. As a result of the survey on students' perceptions of formative assessmenting what content they understood or did not understand. Items that consider Bloom's taxonomy allow students to remember, understand, and apply to clinical contexts.
We aimed to examine the participants' satisfaction and evaluation of the program's appropriateness, outcomes and benefits from participants' perspectives and gather suggestions from students to improve peer mentor programs.
From 2016 to 2018, 67 mentees and mentors participated in the peer mentoring program. All program participants were asked to participate in the survey, and the respondents were invited to focus group interview (FGI). Quantitative data was collected from the survey questionnaire. Qualitative data was gathered from the open-end questions in the survey and supplemented from additional semi-structured FGIs. The interview data were analyzed using qualitative content analysis.
Nineteen responded to the survey, and six participated in the further FGI. Qualitative data contained outcomes and mutual benefits, factors for mentoring success, negative experiences, and suggestions for improvement. Especially factors for mentoring success consisted of various methods of studying assistance, motivad improved relationships. Furthermore, we expect that this program can be improved with participants' suggestions in the future.It is necessary to reflect on the question, "How to prepare for medical education after coronavirus disease 2019 (COVID-19)?" Although we are preparing for the era of Education 4.0 in line with the 4th industrial revolution of artificial intelligence and big data, most measures are focused on the methodologies of transferring knowledge; essential innovation is not being addressed. What is fundamentally needed in medicine is insightful intelligence that can see the invisible. We should not create doctors who only prescribe antispasmodics for abdominal pain, or antiemetic drugs for vomiting. Good clinical reasoning is not based on knowledge alone. Insightology in medicine is based on experience through Bayesian reasoning and imagination through the theory of mind. This refers to diagnosis of the whole, greater than the sum of its parts, by looking at the invisible using the Gestalt strategy. Identifying the missing process that links symptoms is essential. This missing process can be described in one word context. An accurate diagnosis is possible only by understanding context, which can be done by standing in someone else's shoes. From the viewpoint of medicine, Education 4.0 is worrisome because people are still clinging to methodology. The subject we should focus on is "human", not "artificial" intelligence. We should first advance the "insightology in medicine" as a new paradigm, which is the "essence" that will never change even when rare "phenomena" such as the COVID-19 outbreak occur. For this reason, we should focus on teaching insightology in medicine, rather than teaching medical knowledge.
Weight loss through lifestyle modification is recommended for patients with nonalcoholic fatty liver disease (NAFLD). Recent studies have suggested that repeated loss and gain of weight is associated with worse health outcomes. This study aimed to examine the association between weight variability and the risk of NAFLD in patients without diabetes.
We examined the health-checkup data of 30,708 participants who had undergone serial examinations between 2010 and 2014. Weight variability was assessed using coefficient of variation and the average successive variability of weight (ASVW), which was defined as the sum of absolute weight changes between successive years over the 5-year period divided by 4. The participants were classified according to the baseline body mass index and weight difference over 4 years.
On dividing the participants into four groups according to ASVW quartile groups, those in the highest quartile showed a significantly increased risk of NAFLD compared to those in the lowest quartile (odds ratio [OR], 1.89; 95% confidence interval [CI], 1.63 to 2.19). Among participants without obesity at baseline, individuals with high ASVW showed increased risk of NAFLD (OR, 1.80; 95% CI, 1.61 to 2.01). Participants with increased weight over 4 years and high ASVW demonstrated higher risk of NAFLD compared to those with stable weight and low ASVW (OR, 4.87; 95% CI, 4.29 to 5.53).
Regardless of participant baseline obesity status, high weight variability was associated with an increased risk of developing NAFLD. Our results suggest that further effort is required to minimize weight fluctuations after achieving a desirable body weight.
Regardless of participant baseline obesity status, high weight variability was associated with an increased risk of developing NAFLD. Our results suggest that further effort is required to minimize weight fluctuations after achieving a desirable body weight.
Both intra-abdominal fat (IAF) and high-density lipoprotein cholesterol (HDL-C) are known to be associated with cardiometabolic health. We evaluated whether the accumulation of computed tomography (CT)-measured IAF over 5 years was related to baseline HDL-C concentration in a prospective cohort study.
All participants were Japanese-Americans between the ages of 34 and 74 years. Plasma HDL-C concentration and CT measurements of IAF, abdominal subcutaneous fat (SCF), and thigh SCF cross-sectional areas were assessed at baseline and at 5-year follow-up visits.
A total of 397 subjects without diabetes were included. selleckchem The mean±standard deviation HDL-C concentration was 51.6±13.0 mg/dL in men and 66.0±17.0 mg/dL in women, and the IAF was 91.9±48.4 cm2 in men and 63.1±39.5 cm2 in women. The baseline plasma concentration of HDL-C was inversely associated with the change in IAF over 5 years using multivariable regression analysis with adjustment for age, sex, family history of diabetes, weight change over 5 years, and baseline measurements of body mass index, IAF, abdominal SCF, abdominal circumference, thigh SCF, and homeostatic model assessment for insulin resistance.
These results demonstrate that HDL-C concentration significantly predicts future accumulation of IAF over 5 years independent of age, sex, insulin sensitivity, and body composition in Japanese-American men and women without diabetes.
These results demonstrate that HDL-C concentration significantly predicts future accumulation of IAF over 5 years independent of age, sex, insulin sensitivity, and body composition in Japanese-American men and women without diabetes.
Nonalcoholic fatty liver disease (NAFLD) is the most prevalent cause of chronic liver disease worldwide. Type 2 diabetes mellitus (T2DM) is a risk factor that accelerates NAFLD progression, leading to fibrosis and cirrhosis. Thus, here we aimed to develop a simple model to predict the presence of NAFLD based on clinical parameters of patients with T2DM.
A total of 698 patients with T2DM who visited five medical centers were included. NAFLD was evaluated using transient elastography. Univariate logistic regression analyses were performed to identify potential contributors to NAFLD, followed by multivariable logistic regression analyses to create the final prediction model for NAFLD.
Two NAFLD prediction models were developed, with and without serum biomarker use. The non-laboratory model comprised six variables age, sex, waist circumference, body mass index (BMI), dyslipidemia, and smoking status. For a cutoff value of ≥60, the prediction accuracy was 0.780 (95% confidence interval [CI], 0.743 to 0.817). The second comprehensive model showed an improved discrimination ability of up to 0.815 (95% CI, 0.782 to 0.847) and comprised seven variables age, sex, waist circumference, BMI, glycated hemoglobin, triglyceride, and alanine aminotransferase to aspartate aminotransferase ratio. Our non-laboratory model showed non-inferiority in the prediction of NAFLD versus previously established models, including serum parameters.
The new models are simple and user-friendly screening methods that can identify individuals with T2DM who are at high-risk for NAFLD. Additional studies are warranted to validate these new models as useful predictive tools for NAFLD in clinical practice.
The new models are simple and user-friendly screening methods that can identify individuals with T2DM who are at high-risk for NAFLD. Additional studies are warranted to validate these new models as useful predictive tools for NAFLD in clinical practice.
