Dataset Information

Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise.

ABSTRACT: BACKGROUND:Medical schools differ, particularly in their teaching, but it is unclear whether such differences matter, although influential claims are often made. The Medical School Differences (MedDifs) study brings together a wide range of measures of UK medical schools, including postgraduate performance, fitness to practise issues, specialty choice, preparedness, satisfaction, teaching styles, entry criteria and institutional factors. METHOD:Aggregated data were collected for 50 measures across 29 UK medical schools. Data include institutional history (e.g. rate of production of hospital and GP specialists in the past), curricular influences (e.g. PBL schools, spend per student, staff-student ratio), selection measures (e.g. entry grades), teaching and assessment (e.g. traditional vs PBL, specialty teaching, self-regulated learning), student satisfaction, Foundation selection scores, Foundation satisfaction, postgraduate examination performance and fitness to practise (postgraduate progression, GMC sanctions). Six specialties (General Practice, Psychiatry, Anaesthetics, Obstetrics and Gynaecology, Internal Medicine, Surgery) were examined in more detail. RESULTS:Medical school differences are stable across time (median alpha?=?0.835). The 50 measures were highly correlated, 395 (32.2%) of 1225 correlations being significant with p

SUBMITTER: McManus IC

PROVIDER: S-EPMC7222458 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise.

McManus I C IC Harborne Andrew Christopher AC Horsfall Hugo Layard HL Joseph Tobin T Smith Daniel T DT Marshall-Andon Tess T Samuels Ryan R Kearsley Joshua William JW Abbas Nadine N Baig Hassan H Beecham Joseph J Benons Natasha N Caird Charlie C Clark Ryan R Cope Thomas T Coultas James J Debenham Luke L Douglas Sarah S Eldridge Jack J Hughes-Gooding Thomas T Jakubowska Agnieszka A Jones Oliver O Lancaster Eve E MacMillan Calum C McAllister Ross R Merzougui Wassim W Phillips Ben B Phillips Simon S Risk Omar O Sage Adam A Sooltangos Aisha A Spencer Robert R Tajbakhsh Roxanne R Adesalu Oluseyi O Aganin Ivan I Ahmed Ammar A Aiken Katherine K Akeredolu Alimatu-Sadia AS Alam Ibrahim I Ali Aamna A Anderson Richard R Ang Jia Jun JJ Anis Fady Sameh FS Aojula Sonam S Arthur Catherine C Ashby Alena A Ashraf Ahmed A Aspinall Emma E Awad Mark M Yahaya Abdul-Muiz Azri AA Badhrinarayanan Shreya S Bandyopadhyay Soham S Barnes Sam S Bassey-Duke Daisy D Boreham Charlotte C Braine Rebecca R Brandreth Joseph J Carrington Zoe Z Cashin Zoe Z Chatterjee Shaunak S Chawla Mehar M Chean Chung Shen CS Clements Chris C Clough Richard R Coulthurst Jessica J Curry Liam L Daniels Vinnie Christine VC Davies Simon S Davis Rebecca R De Waal Hanelie H Desai Nasreen N Douglas Hannah H Druce James J Ejamike Lady-Namera LN Esere Meron M Eyre Alex A Fazmin Ibrahim Talal IT Fitzgerald-Smith Sophia S Ford Verity V Freeston Sarah S Garnett Katherine K General Whitney W Gilbert Helen H Gowie Zein Z Grafton-Clarke Ciaran C Gudka Keshni K Gumber Leher L Gupta Rishi R Harlow Chris C Harrington Amy A Heaney Adele A Ho Wing Hang Serene WHS Holloway Lucy L Hood Christina C Houghton Eleanor E Houshangi Saba S Howard Emma E Human Benjamin B Hunter Harriet H Hussain Ifrah I Hussain Sami S Jackson-Taylor Richard Thomas RT Jacob-Ramsdale Bronwen B Janjuha Ryan R Jawad Saleh S Jelani Muzzamil M Johnston David D Jones Mike M Kalidindi Sadhana S Kalsi Savraj S Kalyanasundaram Asanish A Kane Anna A Kaur Sahaj S Al-Othman Othman Khaled OK Khan Qaisar Q Khullar Sajan S Kirkland Priscilla P Lawrence-Smith Hannah H Leeson Charlotte C Lenaerts Julius Elisabeth Richard JER Long Kerry K Lubbock Simon S Burrell Jamie Mac Donald JMD Maguire Rachel R Mahendran Praveen P Majeed Saad S Malhotra Prabhjot Singh PS Mandagere Vinay V Mantelakis Angelos A McGovern Sophie S Mosuro Anjola A Moxley Adam A Mustoe Sophie S Myers Sam S Nadeem Kiran K Nasseri Reza R Newman Tom T Nzewi Richard R Ogborne Rosalie R Omatseye Joyce J Paddock Sophie S Parkin James J Patel Mohit M Pawar Sohini S Pearce Stuart S Penrice Samuel S Purdy Julian J Ramjan Raisa R Randhawa Ratan R Rasul Usman U Raymond-Taggert Elliot E Razey Rebecca R Razzaghi Carmel C Reel Eimear E Revell Elliot John EJ Rigbye Joanna J Rotimi Oloruntobi O Said Abdelrahman A Sanders Emma E Sangal Pranoy P Grandal Nora Sangvik NS Shah Aadam A Shah Rahul Atul RA Shotton Oliver O Sims Daniel D Sims Daniel D Smart Katie K Smith Martha Amy MA Smith Nick N Sopian Aninditya Salma AS South Matthew M Speller Jessica J Syer Tom J TJ Ta Ngan Hong NH Tadross Daniel D Thompson Benjamin B Trevett Jess J Tyler Matthew M Ullah Roshan R Utukuri Mrudula M Vadera Shree S Van Den Tooren Harriet H Venturini Sara S Vijayakumar Aradhya A Vine Melanie M Wellbelove Zoe Z Wittner Liora L Yong Geoffrey Hong Kiat GHK Ziyada Farris F Devine Oliver Patrick OP

BMC medicine 20200514 1

<h4>Background</h4>Medical schools differ, particularly in their teaching, but it is unclear whether such differences matter, although influential claims are often made. The Medical School Differences (MedDifs) study brings together a wide range of measures of UK medical schools, including postgraduate performance, fitness to practise issues, specialty choice, preparedness, satisfaction, teaching styles, entry criteria and institutional factors.<h4>Method</h4>Aggregated data were collected for 5 ...[more]

PMID: 32404148

Similar Datasets

Project description:Misconduct during medical school predicts subsequent fitness to practise (FtP) events in doctors, but relatively little is known about which factors are associated with such issues during undergraduate education. This study exploits the newly created UK medical education database (UKMED), with the aim of identifying predictors of conduct or health-related issues that could potentially impair FtP. The findings would have implications for policies related to both the selection and support of medical students.Data were available for 14,379 students obtaining provisional registration with the General Medical Council who started medical school in 2007 and 2008. FtP declarations made by students were available, as were various educational and demographic predictor variables, including self-report 'personality measures' for students who participated in UK Clinical Aptitude Test (UKCAT) pilot studies. Univariable and multivariable logistic regression models were developed to evaluate the predictors of FtP declarations.Significant univariable predictors (p?<?0.05) for conduct-related declarations included male gender, white ethnicity and a non-professional parental background. Male gender (OR 3.07) and higher 'self-esteem' (OR 1.45) were independently associated with an increased risk of a conduct issue. Female gender, a non-professional background, and lower self-reported 'confidence' were, among others, associated with increased odds of a health-related declaration. Only 'confidence' was a significant independent predictor of a health declaration (OR 0.69). Female gender, higher UKCAT score, a non-professional background and lower 'confidence' scores were significant predictors of reported depression, and the latter two variables were independent predictors of declared depression.White ethnicity and UK nationality were associated with increased odds of both conduct and health-related declarations, as were certain personality traits. Students from non-professional backgrounds may be at increased risk of depression and therefore could benefit from targeted support. The small effect sizes observed for the 'personality measures' suggest they would offer little potential benefit for selection, over and above those measures already in use.

Project description:Just-In-Time Teaching (JiTT) active learning pedagogy is utilized by various disciplines, but its value in a professional pharmacy curriculum has not yet been demonstrated. The purpose of our research study is to implement and evaluate JiTT in a Doctor of Pharmacy (PharmD) program. The impetus in implementing JiTT into a PharmD curriculum was to provide students with an out-of-classroom learning opportunity to enhance knowledge-based skills. The current study summarizes the implementation of JiTT in four distinct instances: two iterations of the required courses "Integrated Microbiology and Virology" (Fall 2016 and Fall 2017) and "Integrated Immunology" (Winter 2016-2017 and Winter 2017-2018). JiTT included knowledge-based questions in multiple-choice format, integrated case studies, and student responses prior to the actual lecture session. After the conclusion of each course, students were asked to provide feedback on the utilization of JiTT by way of an anonymous survey. Following the Fall 2016 iteration of the Microbiology & Virology course, students found the integrated case studies to be beneficial (mean = 3.27 out of a maximum of 4, SD = 0.62), and their overall endorsement of JiTT was high (mean = 3.61 out of 4, SD = 0.50). For the other three courses included in this study, the primary dependent variable was the student's average rating of JiTT, rated on a five-point scale. Aggregating the scores from the Fall 2017 iteration of the Integrated Microbiology & Virology course and both instances of the Immunology course, students rated JiTT very favorably (mean = 4.17 out of a maximum of 5, SD = 0.77). Students' performances in JiTT-based courses were compared against non-JiTT-based courses. Analysis of assessment data for student's performance on knowledge-based questions showed JiTT was helpful for student learning and JiTT-based courses had more consistent exam scores compared to non-JiTT-based courses. The current results are a promising initial step in validating the usefulness of JiTT in a pharmacy program and lays the foundation for future studies aimed at a direct comparison between a traditional lecture style and JiTT pedagogy implemented into PharmD curricula.

Dataset Information

Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise.

Publications

Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets