Knowledge, attitudes and practices regarding spinal vascular malformations among doctors in China: a cross-sectional study

中国医生对脊柱血管畸形的认知、态度和实践:一项横断面研究

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Abstract

OBJECTIVE: Knowledge, attitude and practice (KAP) models are essential tools for assessing healthcare professionals' understanding, beliefs and behaviours towards specific health issues. This study aimed to explore the KAP of Chinese doctors in diagnosing and treating spinal vascular malformations (SVM). DESIGN: A web-based cross-sectional survey. SETTING: This study was conducted between October and December 2022 through a self-administered questionnaire. PARTICIPANTS: Participants include full-time doctors who voluntarily participate. Doctors in advanced training, regular training or internships were excluded. PRIMARY AND SECONDARY OUTCOME MEASURES: The KAP scores of Chinese doctors in diagnosing and treating SVM measured by the questionnaire. RESULTS: A total of 517 doctors participated in the study, mostly in Shaanxi, China, working in SVM-relevant departments (n=396) or other departments (n=121). The doctors achieved an average knowledge score of 9.66±1.95 (range: 0-12), attitude score of 22.16±1.71 (range: 6-30) and practice scores of 46.13±5.35 for those in SVM-relevant departments (neurosurgery, orthopaedics and neurology) and 8.50±1.25 for those in other departments, respectively, revealing doctors have adequate knowledge, positive attitude and good practice, and those in SVM-relevant departments showing more adeptness compared with those in other departments. Moreover, multivariate logistic regression analysis showed that knowledge about SVM (OR=1.72, 95% CI 1.11 to 2.65, p=0.015), holding a master's degree (OR=1.85, 95% CI 1.14 to 3.00, p=0.013) and working in orthopaedics (OR=0.34, 95% CI 0.13 to 0.88, p=0.026) were independently associated with good attitude. CONCLUSION: Chinese doctors showed adequate knowledge, moderate attitudes and good practice regarding SVM. A continuing education programme may improve clinical practitioners' ability to manage SVM.

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