The role of deliberate practice in the acquisition of clinical skills

<p>Abstract</p> <p>Background</p> <p>The role of deliberate practice in medical students' development from novice to expert was examined for preclinical skill training.</p> <p>Methods</p> <p>Students in years 1-3 completed 34 Likert type items, adapted from a questionnaire about the use of deliberate practice in cognitive learning. Exploratory factor analysis and reliability analysis were used to validate the questionnaire. Analysis of variance examined differences between years and regression analysis the relationship between deliberate practice and skill test results.</p> <p>Results</p> <p>875 students participated (90%). Factor analysis yielded four factors: planning, concentration/dedication, repetition/revision, study style/self reflection. Student scores on 'Planning' increased over time, score on sub-scale 'repetition/revision' decreased. Student results on the clinical skill test correlated positively with scores on subscales 'planning' and 'concentration/dedication' in years 1 and 3, and with scores on subscale 'repetition/revision' in year 1.</p> <p>Conclusions</p> <p>The positive effects on test results suggest that the role of deliberate practice in medical education merits further study. The cross-sectional design is a limitation, the large representative sample a strength of the study. The vanishing effect of repetition/revision may be attributable to inadequate feedback. Deliberate practice advocates sustained practice to address weaknesses, identified by (self-)assessment and stimulated by feedback. Further studies should use a longitudinal prospective design and extend the scope to expertise development during residency and beyond.</p

Learning physical examination skills outside timetabled training sessions: what happens and why?

Lack of published studies on students’ practice behaviour of physical examination skills outside timetabled training sessions inspired this study into what activities medical students undertake to improve their skills and factors influencing this. Six focus groups of a total of 52 students from Years 1–3 using a pre-established interview guide. Interviews were recorded, transcribed and analyzed using qualitative methods. The interview guide was based on questionnaire results; overall response rate for Years 1–3 was 90% (n = 875). Students report a variety of activities to improve their physical examination skills. On average, students devote 20% of self-study time to skill training with Year 1 students practising significantly more than Year 3 students. Practice patterns shift from just-in-time learning to a longitudinal selfdirected approach. Factors influencing this change are assessment methods and simulated/real patients. Learning resources used include textbooks, examination guidelines, scientific articles, the Internet, videos/DVDs and scoring forms from previous OSCEs. Practising skills on fellow students happens at university rooms or at home. Also family and friends were mentioned to help. Simulated/real patients stimulated students to practise of physical examination skills, initially causing confusion and anxiety about skill performance but leading to increased feelings of competence. Difficult or enjoyable skills stimulate students to practise. The strategies students adopt to master physical examination skills outside timetabled training sessions are self-directed. OSCE assessment does have influence, but learning takes place also when there is no upcoming assessment. Simulated and real patients provide strong incentives to work on skills. Early patient contacts make students feel more prepared for clinical practice

Influence of the workplace on learning physical examination skills

BACKGROUND: Hospital clerkships are considered crucial for acquiring competencies such as diagnostic reasoning and clinical skills. The actual learning process in the hospital remains poorly understood. This study investigates how students learn clinical skills in workplaces and factors affecting this. METHODS: Six focus group sessions with 32 students in Internal Medicine rotation (4–9 students per group; sessions 80–90 minutes). Verbatim transcripts were analysed by emerging themes and coded independently by three researchers followed by constant comparison and axial coding. RESULTS: Students report to learn the systematics of the physical examination, gain agility and become able to recognise pathological signs. The learning process combines working alongside others and working independently with increasing responsibility for patient care. Helpful behaviour includes making findings explicit through patient files or during observation, feedback by abnormal findings and taking initiative. Factors affecting the process negatively include lack of supervision, uncertainty about tasks and expectations, and social context such as hierarchy of learners and perceived learning environment. CONCLUSION: Although individual student experiences vary greatly between different hospitals, it seems that proactivity and participation are central drivers for learning. These results can improve the quality of existing programmes and help design new ways to learn physical examination skills

Data-Driven Extract Method Recommendations: A Study at ING

The sound identification of refactoring opportunities is still an open problem in software engineering. Recent studies have shown the effectiveness of machine learning models in recommending methods that should undergo different refactoring operations. In this work, we experiment with such approaches to identify methods that should undergo an Extract Method refactoring, in the context of ING, a large financial organization. More specifically, we (i) compare the code metrics distributions, which are used as features by the models, between open-source and ING systems, (ii) measure the accuracy of different machine learning models in recommending Extract Method refactorings, (iii) compare the recommendations given by the models with the opinions of ING experts. Our results show that the feature distributions of ING systems and open-source systems are somewhat different, that machine learning models can recommend Extract Method refactorings with high accuracy, and that experts tend to agree with most of the recommendations of the model

Data-Driven Extract Method Recommendations: A Study at ING

The sound identification of refactoring opportunities is still an open problem in software engineering. Recent studies have shown the effectiveness of machine learning models in recommending methods that should undergo different refactoring operations. In this work, we experiment with such approaches to identify methods that should undergo an Extract Method refactoring, in the context of ING, a large financial organization. More specifically, we (i) compare the code metrics distributions, which are used as features by the models, between open-source and ING systems, (ii) measure the accuracy of different machine learning models in recommending Extract Method refactorings, (iii) compare the recommendations given by the models with the opinions of ING experts. Our results show that the feature distributions of ING systems and open-source systems are somewhat different, that machine learning models can recommend Extract Method refactorings with high accuracy, and that experts tend to agree with most of the recommendations of the model

Removing dependencies from large software projects: Are you really sure?

When developing and maintaining large software systems, a great deal of effort goes into dependency management. During the whole lifecycle of a software project, the set of dependencies keeps changing to accommodate the addition of new features or changes in the running environment. Package management tools are quite popular to automate this process, making it fairly easy to automate the addition of new dependencies and respective versions. However, over the years, a software project might evolve in a way that no longer needs a particular technology or dependency. But the choice of removing that dependency is far from trivial: one cannot be entirely sure that the dependency is not used in any part of the project. Hence, developers have a hard time confidently removing dependencies and trusting that it will not break the system in production. In this paper, we propose a decision framework to improve the detection of unused dependencies. Our approach builds on top of the existing dependency analysis tool DepClean. We start by improving the support of Java dynamic features in DepClean. We do so by augmenting the analysis with the state-of-the-art call graph generation tool OPAL. Then, we analyze the potentially unused dependencies detected by classifying their logical relationship with the other components to decide on follow-up steps, which we provide in the form of a decision diagram. Results show that developers can focus their efforts on maintaining bloated dependencies by following the recommendations of our decision framework. When applying our approach to a large industrial software project, we can reduce one-third of false positives when compared to the state-of-the-art. We also validate our approach by analyzing dependencies that were removed in the history of open-source projects. Results show consistency between our approach and the decisions taken by open-source developers.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Software EngineeringSoftware Technolog