Research On The Motivation Of Knowledge Payment From The Perspective Of Active Health

Users’ health motivation can be divided into passive health and active health,which is different from the passive health centered on disease treatment in the traditional medical model.The active health centered on the concept of "preventing diseases" in the new medical model is more in line with people’s pursuit of a better life.In the post-epidemic era,the concept of active health has been deeply rooted in people’s hearts.The progress of science and technology has promoted the rapid development of "Internet+medical service",and the data of users’ consultation in online medical platform is increasing day by day.The text data of online paid consultation hides users’ health motives.The purpose of this article is to explore the motivations behind users paying for knowledge from a proactive health perspective,in order to reveal current user needs for proactive health.This will provide a reference for providers of health knowledge products and services,helping them optimize the quality of their products and services.In this paper,a representative online medical platform in China is taken as an example,and the data of users’ questions in the paid consultation module on the platform are obtained by using the octopus collection tool.The research process is divided into two parts: content coding and model recognition.Content coding is mainly to set the classification standard and category system of the motivation of paying for active health knowledge by combining the existing related research with the sample data characteristics selected in this paper,and then manually standardize the labeling according to the set classification standard,eliminate the data of users’ questions from the perspective of passive health,and build a corpus of the motivation of paying for knowledge from the perspective of active health.Model identification mainly uses BERT model to learn and train on labeled data sets,and makes classification prediction on unlabeled data sets,saving labor costs,and exploring the distribution of users’ motivation types for paying for active health knowledge on larger-scale data.The accuracy,recall and F1 value of the classification model based on BERT are88.7%,83.2% and 82.9%,respectively.The experimental results show that users’ motivation to pay for active health knowledge can be divided into two categories:disease prevention and disease management,among which the distribution ratio of health motivation for disease prevention is: enhancing immunity accounts for 50.76%;Prevention of health risks accounted for 31.26%;Early disease intervention accounted for 8.74%;Early disease detection accounted for 8%;Self-health monitoring accounted for 1.24%.The distribution ratio of health motivation of disease management is: daily maintenance and health care accounts for 33.23%;Self-conditioning recovery accounted for 27.26%;Diet and routine management accounted for 24.05%;Relieving discomfort accounted for 9.84%;Control of disease development accounted for 5.62%.This study has three main contributions.Firstly,a three-level category system for motivation classification of knowledge payment from the perspective of proactive health was constructed,and a corresponding text corpus was established through content analysis.Secondly,a text classification model based on BERT was used to train and predict the data,clarifying the distribution of proactive health payment motivations among users.Finally,through the analysis of the current situation of proactive health knowledge payment motivations among online medical users,optimization strategies for health knowledge and product providers were proposed,including measures such as strengthening targeting,providing personalized customization,increasing diversity,and enhancing user interaction to meet users’ health needs.The results of this study are expected to provide guidance and reference for relevant enterprises and institutions to optimize health knowledge products and services,and to promote competition and development in the health knowledge payment industry.