Exploring factors affecting undergraduate medical students’ study strategies in the clinical years: a qualitative study

The aim of this study is to explore the effects of clinical supervision, and assessment characteristics on the study strategies used by undergraduate medical students during their clinical rotations. We conducted a qualitative phenomenological study at King Saud Bin Abdulaziz University for Health Sciences, College of Medicine, Riyadh, Saudi Arabia during the period from November 2007 to December 2008. We conducted semi-structured focus groups interviews with students and conducted individual interviews with teachers and students to explore students’ and clinical teachers’ perceptions and interpretations of factors influencing students’ study strategies. Data collection was continued until saturation was reached. We used Atlas-ti Computer Software (Version 5.2) to analyse the data, apply the obtained themes to the whole dataset and rearrange the data according to the themes and sub-themes. Analysis of data from interviews with twenty-eight students and thirteen clinical supervisors yielded three major themes relating to factors affecting students’ study strategies: “clinical supervisors and supervision”, “stress and anxiety” and “assessment”. The three themes we identified played a role in students’ adoption of different study strategies in the “community of clinical practice”. It appeared that teachers played a key role, particularly as assessors, clinical supervisors and as a source of stress to students

Impact of clerkship in the attitudes toward psychiatry among Portuguese medical students

<p>Abstract</p> <p>Background</p> <p>Given the shortage of human resources and the launching of a new Mental Health Plan, recruitment of psychiatrists is currently a major concern in Portugal, as well as in several other countries. Medical students' attitude toward psychiatry has been pointed as a predictor of recruitment. This study aims to evaluate the medical students' perception of psychiatry before and after a clerkship, and the impact on their intention to pursue psychiatry as a future specialty option.</p> <p>Methods</p> <p>Two self-report questionnaires were administered to all 6^thyear students in a medical school in Lisbon, before and after a 4-weeks full-time psychiatric clerkship, in order to evaluate attitudes toward psychiatry and intention to follow psychiatry in the future. Statistical analysis included Wilcoxon and Chi-square tests.</p> <p>Results</p> <p>153 students (60.8% female) filled in both questionnaires (no dropouts). After the clerkship, there was a significant improvement regarding the overall merits of psychiatry, efficacy, role definition and functioning of psychiatrists, use of legal powers to hospitalize patients and specific medical school factors. There was also a significant increase of students decided or considering the possibility to take a residency in psychiatry.</p> <p>However, perceptions of low prestige and negative pressure from family and peers regarding a future choice of psychiatry remained unchanged in about one-third of the students.</p> <p>Conclusions</p> <p>The results indicate clearly that the clerkship had a favorable overall impact on the student attitude towards psychiatry, as well as in the number of students considering a future career in psychiatry. Attitudes toward psychiatry seems a promising outcome indicator of the clerkship's quality, but further research is needed in order to assess its reliability as a sound predictor of recruitment.</p

Increased Mast Cell Density and Airway Responses to Allergic and Non-Allergic Stimuli in a Sheep Model of Chronic Asthma

BACKGROUND: Increased mast cell (MC) density and changes in their distribution in airway tissues is thought to contribute significantly to the pathophysiology of asthma. However, the time sequence for these changes and how they impact small airway function in asthma is not fully understood. The aim of the current study was to characterise temporal changes in airway MC density and correlate these changes with functional airway responses in sheep chronically challenged with house dust mite (HDM) allergen. METHODOLOGY/PRINCIPAL FINDINGS: MC density was examined on lung tissue from four spatially separate lung segments of allergic sheep which received weekly challenges with HDM allergen for 0, 8, 16 or 24 weeks. Lung tissue was collected from each segment 7 days following the final challenge. The density of tryptase-positive and chymase-positive MCs (MC(T) and MC(TC) respectively) was assessed by morphometric analysis of airway sections immunohistochemically stained with antibodies against MC tryptase and chymase. MC(T) and MC(TC) density was increased in small bronchi following 24 weeks of HDM challenges compared with controls (P<0.05). The MC(TC)/MC(T) ratio was significantly increased in HDM challenged sheep compared to controls (P<0.05). MC(T) and MC(TC) density was inversely correlated with allergen-induced increases in peripheral airway resistance after 24 weeks of allergen exposure (P<0.05). MC(T) density was also negatively correlated with airway responsiveness after 24 challenges (P<0.01). CONCLUSIONS: MC(T) and MC(TC) density in the small airways correlates with better lung function in this sheep model of chronic asthma. Whether this finding indicates that under some conditions mast cells have protective activities in asthma, or that other explanations are to be considered requires further investigation

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Clustering South African households based on their asset status using latent variable models

The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and described by clustering the households into homogeneous groups based on their asset status. A model-based approach to clustering the Agincourt households, based on latent variable models, is proposed. In the case of modeling binary or ordinal items, item response theory models are employed. For nominal survey items, a factor analysis model, similar in nature to a multinomial probit model, is used. Both model types have an underlying latent variable structure—this similarity is exploited and the models are combined to produce a hybrid model capable of handling mixed data types. Further, a mixture of the hybrid models is considered to provide clustering capabilities within the context of mixed binary, ordinal and nominal response data. The proposed model is termed a mixture of factor analyzers for mixed data (MFA-MD). The MFA-MD model is applied to the survey data to cluster the Agincourt households into homogeneous groups. The model is estimated within the Bayesian paradigm, using a Markov chain Monte Carlo algorithm. Intuitive groupings result, providing insight to the different socio-economic strata within the Agincourt region.Science Foundation IrelandNIH grantsGoogle Faculty Research Awar