Cross-sectional Survey of Medical student perceptions of And desires for Research and Training pathways (SMART): an analysis of prospective cohort study of UK medical students

Objective: Clinician-scientists are critical to medical innovation and research. However, the number of clinician scientists in the UK has been declining steadily over the last decade. One of the cited reasons is poor student recruitment to academic training pathways. The SMART study aims to assess current student perceptions on research and identify key factors influencing whether a student is interested in research. Design: We conducted a cross-sectional survey study between January and May 2022. Setting: This was a multi-centre national study with data collected across 40 universities offering medical courses in the UK. Participants: Participants were UK medical students enrolled in medicine for 21/22 academic year. Main outcome and measure: The main outcomes were related to participant perceptions on research and whether they were interested in engaging with research in their future career. These measures were correlated with demographic and non-demographic details using regression analyses. Results: One thousand seven hundred seventy-four individuals participated in the SMART survey from 40 medical schools. Nearly half the participants felt there were barriers preventing them from doing research (46.67%) and almost three-quarters felt it was at least somewhat difficult to combine research with medical school (73.49%). Of the options available, most commonly students did not want to pursue an academic career (43.11%) or training pathway (42.49%). However, most participants felt it was useful to do research at medical school (59.54%) and were also interested in doing more research in the future (69.16%). Regression analysis identified many factors influencing student’s perceptions of research including year of study, gender, socioeconomic status, family background, research exposure at medical school, ethnicity, and country of pre-university education. Conclusions: The SMART study is the first of its kind in the UK, shedding light on medical student perceptions. While some express strong interest in academic careers, a larger proportion show a broader interest in research. Demographic factors like gender, parental occupation, and socioeconomic status play a role. Further exploration is needed for specific groups to address barriers, promote research, and boost academic pathway recruitment

Genomics-informed outbreak investigations of SARS-CoV-2 using civet

The scale of data produced during the SARS-CoV-2 pandemic has been unprecedented, with more than 13 million sequences shared publicly at the time of writing. This wealth of sequence data provides important context for interpreting local outbreaks. However, placing sequences of interest into national and international context is difficult given the size of the global dataset. Often outbreak investigations and genomic surveillance efforts require running similar analyses again and again on the latest dataset and producing reports. We developed civet (cluster investigation and virus epidemiology tool) to aid these routine analyses and facilitate virus outbreak investigation and surveillance. Civet can place sequences of interest in the local context of background diversity, resolving the query into different ’catchments’ and presenting the phylogenetic results alongside metadata in an interactive, distributable report. Civet can be used on a fine scale for clinical outbreak investigation, for local surveillance and cluster discovery, and to routinely summarise the virus diversity circulating on a national level. Civet reports have helped researchers and public health bodies feedback genomic information in the appropriate context within a timeframe that is useful for public health

Genomics-informed outbreak investigations of SARS-CoV-2 using civet.

Funder: Trinidad and Tobago - UWI Research Development Impact FundThe scale of data produced during the SARS-CoV-2 pandemic has been unprecedented, with more than 13 million sequences shared publicly at the time of writing. This wealth of sequence data provides important context for interpreting local outbreaks. However, placing sequences of interest into national and international context is difficult given the size of the global dataset. Often outbreak investigations and genomic surveillance efforts require running similar analyses again and again on the latest dataset and producing reports. We developed civet (cluster investigation and virus epidemiology tool) to aid these routine analyses and facilitate virus outbreak investigation and surveillance. Civet can place sequences of interest in the local context of background diversity, resolving the query into different 'catchments' and presenting the phylogenetic results alongside metadata in an interactive, distributable report. Civet can be used on a fine scale for clinical outbreak investigation, for local surveillance and cluster discovery, and to routinely summarise the virus diversity circulating on a national level. Civet reports have helped researchers and public health bodies feedback genomic information in the appropriate context within a timeframe that is useful for public health