Journal of evaluation in clinical practice
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Objective Structured Clinical Examinations (OSCEs) are widely used for assessing clinical competence, especially in high-stakes environments such as medical licensure. However, the reuse of OSCE cases across multiple administrations raises concerns about parameter stability, known as item parameter drift (IPD). AIMS & OBJECTIVES: This study aims to investigate IPD in reused OSCE cases while accounting for examiner scoring effects using a Many-facet Rasch Measurement (MFRM) model. ⋯ These findings suggest that while OSCE cases demonstrate sufficient stability for reuse, continuous monitoring is essential to ensure the accuracy of score interpretations and decisions. The study provides an objective threshold for detecting concerning levels of IPD and underscores the importance of addressing examiner scoring effects in OSCE assessments. The MFRM model offers a robust framework for tracking and mitigating IPD, contributing to the validity and reliability of OSCEs in evaluating clinical competence.
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Recognising and responding swiftly to patient deterioration is critical for preventing adverse events. Junior nurses play a vital role in identifying the signs of clinical deterioration and initiating interventions. No prior studies have assessed junior nurses' abilities to manage clinical deterioration in Malawi. ⋯ This study highlights the need for specialised training programmes related to clinical deterioration that incorporate active learning, such as clinical scenarios and practical applications, along with mentorship initiatives to enhance junior nurses' skills and confidence in recognising and responding to clinical deterioration.
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This research aims to develop an effective algorithm for diagnosing COVID-19 in chest X-rays using the transfer learning method and support vector machines. ⋯ This study confirms the importance of applying machine learning methods in medical applications and opens new perspectives for early diagnosis of infectious diseases. The practical application of the obtained results can enhance the efficiency of diagnosis and control the spread of COVID-19, as well as contribute to the development of innovative methods in medical practice.
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Cancer patients experience substantial psychological distress which causes the reduction of the quality of life. However, the risk of psychological distress has not been well predicted especially in young- and middle-aged gynaecological cancer patients. This study aimed to develop a prediction model for psychological distress in young- and middle-aged gynaecological cancer patients using the artificial neural network (ANN). ⋯ Compared with the LR model, the ANN model shows obvious superiority across all assessment index outcomes, and it may be used as a decision-support tool for early identification of young- and middle-aged gynaecological cancer patients suffering from psychological distress.
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Chewing is a fundamental motor activity, but there is no specific assessment tool in Italian for paediatric rehabilitation. The Karaduman Chewing Performance Scale (KCPS) is a performance-based assessment tool that allow to classify chewing performance in childhood. ⋯ Despite limited sample in reliability analysis and the need of exploring the relationship with chewing abilities and severity of diseases, the KCPS was found a reliable and valid tool for determining the level of chewing performance in paediatric population. Now Italian clinicians can use it with more confidence in their clinical practice and research.