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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Healthcare professionals' knowledge, attitudes, and practices towards predictive diagnosis of early neurological
Xin Xia1, Feng Li1, Binbin Yuan1
1Department of Neurology, Jiangsu Rugao Boai Hospital, Nantong, 226500, Jiangsu, China.
Abstract:
This study aimed to investigate healthcare professionals' knowledge, attitudes, and practices (KAP) towards predictive diagnosis of early neurological deterioration. We conducted a cross-sectional study at hospitals in Jiangsu region from December 1, 2023, to April 15, 2024. Demographic information and KAP scores were evaluated using a self-designed KAP questionnaire. A total of 299 valid responses were included in the final analysis. The median scores for knowledge, attitude, and practice were 44.00 [37.00, 47.00], 18.00 [17.00, 19.00], and 35.00 [31.00, 37.00], respectively. A total of 40.2% rarely used clinical scales, whereas 28.5% rarely relied on imaging for assessing early neurological deterioration risk. Multivariate logistic regression analysis revealed independent associations between poor knowledge and lack of professional title, other positions, and working in the ICU. Additionally, holding a college or Bachelor's degree was independently linked to a negative attitude. Higher knowledge scores and holding doctor positions were independently associated with practice. Healthcare professionals generally possess adequate knowledge and demonstrate positive attitudes and proactive practices towards the predictive diagnosis of early neurological deterioration. Efforts to improve stroke predictive diagnosis should focus on targeted educational programs that address specific gaps in knowledge and attitudes, especially for professionals in critical care settings and those without advanced titles.
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