Dataset Information

Attitudes of the public and medical professionals toward nurse prescribing: A text-mining study based on social medias.

ABSTRACT:

Objectives

This study aimed to explore the public and medical professionals' concerns and attitudes toward nurse prescribing using text-mining method to analyze social media data.

Methods

Python was used to automatically mine data related to the keywords "nurse prescribing" and "prescription" that were posted on four Chinese internet platforms between January 1, 2017, and November 1, 2022. The four Chinese internet platforms included social media sites such as Zhihu and Weibo, as well as medical forums like Aiaiyi Medical Hotspot and Dingxiangyuan Medicine. We conducted personnel, topic, and sentiment analysis techniques using SnowNLP, Bayesian Latent Dirichlet Allocation (LDA), and BosonNLP. Finally, we conducted content analysis using Nvivo 11 based on the results of the topic and sentiment analysis to obtain comprehensive and insightful results.

Results

We acquired 2,823 comments totaling 92,859 words on the four Internet platforms to conduct analysis. The analyze result showed that many public and medical professionals held a negative attitude toward nurse prescribing, and few had a prudent positive attitude. The public is concerned about the impact of nurse prescribing on medical professionals and the competency requirements for nurses. Medical professionals are concerned about the current and future status of nurse prescribing in China and the difficulties in implementing nurse prescribing.

Conclusion

Nurses should gradually gain recognition for their expertise and win the acceptance of the public and medical professionals on their ability of nursing prescribing by striving to enhance their professional capacity and self-authorization capabilities. Nurse administrators and educators need to recognize the advantages of nurse prescribing and address the challenges and issues in its implementation through promoting legislation, education, and heightening public awareness of its benefits.

SUBMITTER: Zhou Q

PROVIDER: S-EPMC10859581 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Attitudes of the public and medical professionals toward nurse prescribing: A text-mining study based on social medias.

Zhou Qi Q Xu Yiqing Y Yang Lili L Menhas Rashid R

International journal of nursing sciences 20231213 1

<h4>Objectives</h4>This study aimed to explore the public and medical professionals' concerns and attitudes toward nurse prescribing using text-mining method to analyze social media data.<h4>Methods</h4>Python was used to automatically mine data related to the keywords "nurse prescribing" and "prescription" that were posted on four Chinese internet platforms between January 1, 2017, and November 1, 2022. The four Chinese internet platforms included social media sites such as Zhihu and Weibo, as ...[more]

PMID: 38352288

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Project description:BackgroundCOVID-19 is still rampant all over the world. Until now, the COVID-19 vaccine is the most promising measure to subdue contagion and achieve herd immunity. However, public vaccination intention is suboptimal. A clear division lies between medical professionals and laypeople. While most professionals eagerly promote the vaccination campaign, some laypeople exude suspicion, hesitancy, and even opposition toward COVID-19 vaccines.ObjectiveThis study aims to employ a text mining approach to examine expression differences and thematic disparities between the professionals and laypeople within the COVID-19 vaccine context.MethodsWe collected 3196 answers under 65 filtered questions concerning the COVID-19 vaccine from the China-based question and answer forum Zhihu. The questions were classified into 5 categories depending on their contents and description: adverse reactions, vaccination, vaccine effectiveness, social implications of vaccine, and vaccine development. Respondents were also manually coded into two groups: professional and laypeople. Automated text analysis was performed to calculate fundamental expression characteristics of the 2 groups, including answer length, attitude distribution, and high-frequency words. Furthermore, structural topic modeling (STM), as a cutting-edge branch in the topic modeling family, was used to extract topics under each question category, and thematic disparities were evaluated between the 2 groups.ResultsLaypeople are more prevailing in the COVID-19 vaccine-related discussion. Regarding differences in expression characteristics, the professionals posted longer answers and showed a conservative stance toward vaccine effectiveness than did laypeople. Laypeople mentioned countries more frequently, while professionals were inclined to raise medical jargon. STM discloses prominent topics under each question category. Statistical analysis revealed that laypeople preferred the "safety of Chinese-made vaccine" topic and other vaccine-related issues in other countries. However, the professionals paid more attention to medical principles and professional standards underlying the COVID-19 vaccine. With respect to topics associated with the social implications of vaccines, the 2 groups showed no significant difference.ConclusionsOur findings indicate that laypeople and professionals share some common grounds but also hold divergent focuses toward the COVID-19 vaccine issue. These incongruities can be summarized as "qualitatively different" in perspective rather than "quantitatively different" in scientific knowledge. Among those questions closely associated with medical expertise, the "qualitatively different" characteristic is quite conspicuous. This study boosts the current understanding of how the public perceives the COVID-19 vaccine, in a more nuanced way. Web-based question and answer forums are a bonanza for examining perception discrepancies among various identities. STM further exhibits unique strengths over the traditional topic modeling method in statistically testing the topic preference of diverse groups. Public health practitioners should be keenly aware of the cognitive differences between professionals and laypeople, and pay special attention to the topics with significant inconsistency across groups to build consensus and promote vaccination effectively.

Dataset Information

Attitudes of the public and medical professionals toward nurse prescribing: A text-mining study based on social medias.

Objectives

Methods

Results

Conclusion

Publications

Attitudes of the public and medical professionals toward nurse prescribing: A text-mining study based on social medias.

Similar Datasets

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