A cross-sectional survey on evidence-based nursing practice for pain assessment by nurses in China's class 3A hospitals
Objective To explore the current situation of evidence-based nursing practice in pain assess-ment by nurses in China to provide the decision-making data for maximizing to relieve the patient pain by car-rying out the pain assessment evidence-based nursing practice.Methods The non-probability sampling meth-od was used to conduct an online anonymous survey in 63 class 3A hospitals in the whole country.The ques-tionnaire included the general information questionnaire and evidence-based nursing practice scale for pain as-sessment.The evidence-based nursing practice scale for pain assessment included the pain screening,compre-hensive pain assessment,exchange with the patients and their households in the pain assessment,pain re-as-sessment,pain assessment tool selection and record.The 5 dimensions were compared by using item equaliza-tion.The data analysis was performed by the SPSS26.0.Results A total of 1 518 questionnaires were recov-ered,in which 1 482 questionnaires were valid with an effective recovery rate of 97.62％.The evidence-based nurse practice of pain assessment by nurse was(108.40±17.96)points,the pain screening was(12.87±2.23)points,the item average score was the highest[(4.29±0.74)points],the communication with the patients and their household was(23.69±4.93)points and the item average score was the lowest[(3.94±0.82)points].The regression analysis showed that whether receiving the pain training and whether distinguishing active pain and resting pain had a positive effect on the practical behavior(P＜0.05).Conclusion The evi-dence-based nursing practice in pain assessment by nurses in the partial class 3A hospitals in China is in the upper medium level.However,the communication between the patients and their families is insufficient.Man-agers should constantly enrich the training content and methods,and guide nurses to strengthen the communi-cation between nurses and the patients.
国家科技期刊平台
NSTL
万方数据
维普
