Aim: The aim of this review is to summarise the evidence currently available on role modelling by doctors in medical education. Methods: A systematic search of electronic databases was conducted (PubMed, Psyc-Info, Embase, Education Research Complete, Web of Knowledge, ERIC and British Education Index) from January 1990 to February 2012. Data extraction was completed by two independent reviewers and included a quality assessment of each paper. A thematic analysis was conducted on all the included papers. Results: Thirty-nine studies fulfilled the inclusion criteria for the review. Six main themes emerged from the content of high and medium quality papers: 1) the attributes of positive doctor role models; 2) the personality profiles of positive role models; 3) the influence of positive role models on students' career choice; 4) the process of positive role modelling; 5) the influence of negative role modelling; 6) the influence of culture, diversity and gender in the choice of role model. Conclusions: This systematic review highlights role modelling as an important process for the professional development of learners. Excellence in role modelling involves demonstration of high standards of clinical competence, excellence in clinical teaching skills and humanistic personal qualities. Positive role models not only help to shape the professional development of our future physicians, they also influence their career choices. This review has highlighted two main challenges in doctor role modelling: the first challenge lies in our lack of understanding of the complex phenomenon of role modelling. Second, the literature draws attention to negative role modelling and this negative influence requires deeper exploration to identify ways to mitigate adverse effects. This BEME review offers a preliminary guide to future discovery and progress in the area of doctor role modelling. Section 1: IntroductionRole modelling has been highlighted as an important phenomenon in medical education. Its importance in professional development of learners has been illustrated by medical educators' worldwide (Gordon & Lyon 1998;Skeff & Mutha, 1998; Ficklin et al. 1998;Yazigi et al. 2006;Joubert et al. 2006;McLean, 2006). Over the past decade there has been an explosion of interest in doctor role modelling with many influential discussion articles (Matthews 2000;Maudsley 2001;Paice et al. 2002;Kenny et al. 2003;Kahn 2008;Cruess et al. 2008). These leading articles inspired this review of the primary research on role modelling. Role modelling has been described as the process in which 'faculty members demonstrate clinical skills, model and articulate expert thought processes and manifest positive professional characteristics. ' (Irby 1986, p. 40). This is the definition that was chosen for this systematic review. Role modelling takes place in three interrelated educational environments which are the formal, informal and hidden curriculum (Hafferty 1998). The informal curriculum is defined as an 'unscripted, predominantly ad hoc, and hi...

SummaryBackgroundStudies evaluating titration of antihypertensive medication using self-monitoring give contradictory findings and the precise place of telemonitoring over self-monitoring alone is unclear. The TASMINH4 trial aimed to assess the efficacy of self-monitored blood pressure, with or without telemonitoring, for antihypertensive titration in primary care, compared with usual care.MethodsThis study was a parallel randomised controlled trial done in 142 general practices in the UK, and included hypertensive patients older than 35 years, with blood pressure higher than 140/90 mm Hg, who were willing to self-monitor their blood pressure. Patients were randomly assigned (1:1:1) to self-monitoring blood pressure (self-montoring group), to self-monitoring blood pressure with telemonitoring (telemonitoring group), or to usual care (clinic blood pressure; usual care group). Randomisation was by a secure web-based system. Neither participants nor investigators were masked to group assignment. The primary outcome was clinic measured systolic blood pressure at 12 months from randomisation. Primary analysis was of available cases. The trial is registered with ISRCTN, number ISRCTN 83571366.Findings1182 participants were randomly assigned to the self-monitoring group (n=395), the telemonitoring group (n=393), or the usual care group (n=394), of whom 1003 (85%) were included in the primary analysis. After 12 months, systolic blood pressure was lower in both intervention groups compared with usual care (self-monitoring, 137·0 [SD 16·7] mm Hg and telemonitoring, 136·0 [16·1] mm Hg vs usual care, 140·4 [16·5]; adjusted mean differences vs usual care: self-monitoring alone, −3·5 mm Hg [95% CI −5·8 to −1·2]; telemonitoring, −4·7 mm Hg [–7·0 to −2·4]). No difference between the self-monitoring and telemonitoring groups was recorded (adjusted mean difference −1·2 mm Hg [95% CI −3·5 to 1·2]). Results were similar in sensitivity analyses including multiple imputation. Adverse events were similar between all three groups.InterpretationSelf-monitoring, with or without telemonitoring, when used by general practitioners to titrate antihypertensive medication in individuals with poorly controlled blood pressure, leads to significantly lower blood pressure than titration guided by clinic readings. With most general practitioners and many patients using self-monitoring, it could become the cornerstone of hypertension management in primary care.FundingNational Institute for Health Research via Programme Grant for Applied Health Research (RP-PG-1209-10051), Professorship to RJM (NIHR-RP-R2-12-015), Oxford Collaboration for Leadership in Applied Health Research and Care, and Omron Healthcare UK.

Objective To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice. Design Systematic review of test accuracy studies. Data sources Medline, Embase, Web of Science, and Cochrane Database of Systematic Reviews from 1 January 2010 to 17 May 2021. Eligibility criteria Studies reporting test accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in women’s digital mammograms in screening practice, or in test sets. Reference standard was biopsy with histology or follow-up (for screen negative women). Outcomes included test accuracy and cancer type detected. Study selection and synthesis Two reviewers independently assessed articles for inclusion and assessed the methodological quality of included studies using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A single reviewer extracted data, which were checked by a second reviewer. Narrative data synthesis was performed. Results Twelve studies totalling 131 822 screened women were included. No prospective studies measuring test accuracy of AI in screening practice were found. Studies were of poor methodological quality. Three retrospective studies compared AI systems with the clinical decisions of the original radiologist, including 79 910 women, of whom 1878 had screen detected cancer or interval cancer within 12 months of screening. Thirty four (94%) of 36 AI systems evaluated in these studies were less accurate than a single radiologist, and all were less accurate than consensus of two or more radiologists. Five smaller studies (1086 women, 520 cancers) at high risk of bias and low generalisability to the clinical context reported that all five evaluated AI systems (as standalone to replace radiologist or as a reader aid) were more accurate than a single radiologist reading a test set in the laboratory. In three studies, AI used for triage screened out 53%, 45%, and 50% of women at low risk but also 10%, 4%, and 0% of cancers detected by radiologists. Conclusions Current evidence for AI does not yet allow judgement of its accuracy in breast cancer screening programmes, and it is unclear where on the clinical pathway AI might be of most benefit. AI systems are not sufficiently specific to replace radiologist double reading in screening programmes. Promising results in smaller studies are not replicated in larger studies. Prospective studies are required to measure the effect of AI in clinical practice. Such studies will require clear stopping rules to ensure that AI does not reduce programme specificity. Study registration Protocol registered as PROSPERO CRD42020213590.