Search

Objective: This study explores the health information needs of individuals with autism spectrum disorder (ASD) and their caregivers in the post-COVID-19 era by analyzing discussions from Reddit, a popular social media platform. Methods: Utilizing a mixed-method approach that integrates qualitative content analysis with quantitative sentiment analysis, we analyzed user-generated content from the "r/autism" subreddit to identify recurring themes and sentiments. Results: The qualitative analysis uncovered key themes, including symptoms, diagnostic challenges, caregiver experiences, treatment options, and stigma, reflecting the diverse concerns within the ASD community. The quantitative sentiment analysis revealed a predominance of positive sentiment across discussions, although significant instances of neutral and negative sentiments were also present, indicating varied experiences and perspectives among community members. Among the machine learning models used for sentiment classification, the Bi-directional Long Short-Term Memory (Bi-LSTM) model achieved the highest performance, demonstrating a validation accuracy of 95.74%. Conclusions: The findings highlight the need for improved digital platforms and community resources to address the specific health information needs of the ASD community, particularly in enhancing access to reliable information and fostering supportive environments. These insights can guide future interventions and policies aimed at improving the well-being of autistic persons and their caregivers. [ABSTRACT FROM AUTHOR]

Scientific consensus affirms human activity, particularly carbon emissions from market participants, drives global warming. Foreign investment, crucial for sustainability in developing nations, now faces scrutiny regarding its impact on environmental quality in emerging economies. This study examines the influence of Chinese Outward Foreign Direct Investment (OFDI) and fintech on environmental conditions in the top five Chinese-invested African economies, alongside factors such as energy consumption, economic performance, and unemployment affecting CO2 pollution. Quarterly data from 2006–2021 confirm cointegration among variables via panel unit root and cointegration tests. Panel ARDL method estimates coefficients for short and long-run effects. Our findings reveal: (1) A 1% increase in Chinese investment leads to a 0.56% decrease in CO2 emissions, supporting its positive environmental impact. (2) Fintech adoption also demonstrates a beneficial effect, with a 1% increase associated with a 0.18% reduction in CO2 levels. (3) Total energy consumption, as expected, has a detrimental impact, causing a 0.92% increase in CO2 emissions with a 1% rise. (4) Interestingly, economic growth fosters environmental sustainability, while unemployment correlates negatively with it. These findings suggest that targeted Chinese investments and fintech adoption can aid in mitigating CO2 pollution in African economies while balancing economic considerations. [ABSTRACT FROM AUTHOR]