Assessing attitudes of fourth year medical students towards psychiatry and mental illness

Background: Research revealed a high prevalence of negative attitudes towards psychiatry and mental illness among medical students prior to formal psychiatric education. Anti-stigma interventions at the medical student level have been postulated to reduce the risk of negative attitudes, which may drive stigmatization impacting recruitment into training posts and overall medical care. Aim: To determine the prevalence of negative attitudes towards psychiatry and mental illness in a sample of fourth-year medical students prior to formal psychiatric teaching. To ascertain possible sociodemographic correlations with findings. Setting: The University of the Witwatersrand. Methods: A cross-sectional, quantitative, descriptive study was conducted using the Mental Illness: Clinicians’ Attitudes Scale 2 questionnaire and a socio-demographic questionnaire. Results: Of the total scores, 97.2% participants fell below the median potential score of 56, reflecting a low prevalence of stigmatising attitudes. The African cohort expressed less interest in psychiatry (P=0.0017), compared to other race cohorts (ranging from 92.1% to 100.0%). Conclusion: This study revealed a low prevalence of negative and stigmatising attitudes towards psychiatry and mental illness. Of statistical significance, was a relative difference in attitudes towards psychiatry and mental illness in different race cohorts (P=0.0017); however, overall race cohorts showed a low prevalence of negative and stigmatising attitudes towards psychiatry. Contribution: This study creates awareness of the impact factors on attitudes of medical students towards mental illness and specialization in psychiatry

Long‐distance swimming by African lions in Uganda

Earth's most imperiled and iconic wildlife are facing tough decisions under increasing human pressure and limited resources. Swimming across rivers and water bodies filled with high densities of predators may be one such example. In African lions Panthera leo, previous water crossings (recorded in the peer‐reviewed and gray literature, on film, and found using Google Search, and YouTube) have recorded distances ranging from 1 km across Uganda's Kazinga channel located in the Queen Elizabeth National Park six times, and recorded this behavior on film on February 1st 2024. We speculate that three factors could be driving these lions to take long‐distance swims with a high density of crocodiles and hippos Hippopotamus amphibius, namely (1) the lack of lionesses in this ecosystem, (2) intraspecific fights over territory with other male coalitions, and (3) the only other land connection giving lions access to the peninsula is a small road bridge with a strong human presence

A dual process account of creative thinking

This article explicates the potential role played by type 1 thinking (automatic, fast) and type 2 thinking (effortful, logical) in creative thinking. The relevance of Evans's (2007) models of conflict of dual processes in thinking is discussed with regards to creative thinking. The role played by type 1 thinking and type 2 thinking during the different stages of creativity (problem finding and conceptualization, incubation, illumination, verification and dissemination) is discussed. It is proposed that although both types of thinking are active in creativity, the extent to which they are active and the nature of their contribution to creativity will vary between stages of the creative process. Directions for future research to test this proposal are outlined; differing methodologies and the investigation of different stages of creative thinking are discussed. © Taylor & Francis Group, LLC

Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation

We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (ALADIN). We particularly look at the Multi-Layer Perceptron. After optimizing our architecture with ALADIN and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model ANN/ARMA is 14.9% compared to 26.2% for the na\"ive persistence predictor. Note that in the stand alone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposedComment: Energy (2012)