ABSTRACT
Purpose: This study aimed to develop and validate a questionnaire to measure the knowledge, attitude, and practice of parents regarding antibiotic use in Indonesian children using structural equation modeling (SEM) analysis. Methods: The instrument development process was conducted from January 5 to 19, 2023, using the following steps: 1) literature review and item development, 2) internal review and refinement, 3) structural model analysis, and 4) measurement models' reliability and validity. A convenience sample was used to recruit parents as participants from Arcamanik District, Bandung, Indonesia. A total of 83 respondents completed the on-site interview questionnaire. Furthermore, statistical analyses were performed using SPSS Version 21.0 and Analysis of Moment Structures (AMOS) Version 26.0. Results: The content validity for the scales was over 50%, and the reliabilities for the 38 items of the questionnaire were above 0.6, respectively. The suitability of the model was assessed, and the findings showed parameters for indicators: chi-square = 0.0004, CFI = 0.977, RMSEA = 0.044, CMIN/DF = 1.162, AGFI = 0.651, TLI = 0.973, and NFI = 0.860. The GFI parameter did not fit with the output value of 0.718, while the convergent and divergent validity of scores provided evidence in the expected direction. Conclusion: This psychometric development study provides preliminary evidence that the 38-item scales were reliable and valid for assessing knowledge, attitude, and practice toward parents in the self-medication of antibiotics in children.
ABSTRACT
Background: XBB.1.5 is a new subvariant of the SARS-CoV-2 Omicron variant with increased transmissibility and immune escape potential. Twitter has been used to share information and assess this subvariant. Objectives: This study aims to investigate the channel graph, key influencers, top sources, most trends, and pattern discussion, as well as sentiment measures related to Covid-19 XBB.1.5 variant, by using social network analysis (SNA). Methods: This experiment involved the collection of Twitter data through the keywords, "XBB.1.5â³, and NodeXL, with the obtained information subsequently cleaned to remove duplication and irrelevant tweets. SNA was also performed by using analytical metrics to identify influential users and understand the patterns of connectivity among those discussing XBB.1.5. on Twitter. Moreover, the results were visualized through Gephi software, with sentiment analysis performed by using Azure Machine Learning to categorize tweets into three categories, namely positive, negative, and neutral. Results: A total of 43,394 XBB.1.5-based tweets were identified, with five key users observed with the highest betweenness centrality score (BCS), namely "ojimakohei"(red), mikito_777 (blue), "nagunagumomo" (green), "erictopol" (orange), w2skwn3 (yellow). The other hand, the in-degree, out-degree, betweenness, closeness, and eigenvector centrality scores of the top 10 Twitter users to explain various patterns and trends and "ojimakohei" was highly central in the network. Most of the top domains (sources) used in XBB.1.5 discourse originated from Twitter, Japanese websites (co.jp and or.jp), and scientific analysis links (biorxiv.org and cdc.gov). This analysis indicated that most of the tweets (61.35 %) were positively classified, accompanied by neutral (22.44 %) and negative (16.20 %) sentiments. Conclusion: Japan was actively engaged in evaluating the XBB.1.5 variant, with influential users playing a crucial role. The preference for sharing verified sources and the positive sentiment demonstrated a commitment to health awareness. We recommend fostering collaborations between health organizations, the government, and Twitter influencers to address COVID-19-related misinformation and its variants.
