Background: The clinical competency test (CCT) was introduced by the Irish College of General Practitioners (ICGP) in 2015. Similar to the clinical skills assessment (CSA) of the Membership of the Royal College of General Practitioners exam (MRCGP), the CCT is a modified objective structured clinical examination (OSCE).
Aim: The aim of this study was to evaluate the MICGP CCT from the candidates' perspective, to gain an insight into their views of its fairness, relevance, and acceptability.
Design & Setting: This mixed-methods study was conducted with GP registrars in Ireland.
Method: The study was conducted in two phases. Firstly, focus groups were conducted with participants who had previously undertaken the CCT to explore their experience of the CCT. Secondly, findings from the focus groups informed the development of an online questionnaire, which was sent to all GP registrars who completed the CCT in the 2017 summer sitting.
Results: Two focus groups were held with a total of nine participants. Following this, the online questionnaire was emailed to 134 registrars. Of these, 83 registrars completed the questionnaire in full. Registrars reported that the CCT is a fair exam and is relevant to daily general practice. They considered the exam to be a comprehensive assessment that has a positive educational impact. However, they were challenged by time restrictions, and found it financially and emotionally stressful.
Conclusion: This is the first study to evaluate the candidate's perspective of an exiting GP membership exam in the UK or Ireland. The CCT is well-regarded by registrars. The study results will help to inform the future development of the CCT exam.
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http://dx.doi.org/10.3399/bjgpopen18X101605 | DOI Listing |
Community Dent Oral Epidemiol
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School of Clinical Dentistry, University of Sheffield, Sheffield, UK.
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View Article and Find Full Text PDFCureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
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Unlabelled: Transparent and accurate reporting in early phase dose-finding (EPDF) clinical trials is crucial for informing subsequent larger trials. The SPIRIT statement, designed for trial protocol content, does not adequately cover the distinctive features of EPDF trials. Recent findings indicate that the protocol contents in past EPDF trials frequently lacked completeness and clarity.
View Article and Find Full Text PDFMalawi Med J
January 2025
Department of Health Systems and Policy, School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi.
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