Exploring the Functioning of Online Self-Organizations during Public Health Emergencies: Patterns and Mechanism

The influence of service performance in China's sci-tech commissioner system: Using social network analysis and interpretable machine learning

The Sci-Tech Commissioner System (SCS) is a result of exploratory efforts by the Chinese government to use science and technology to strengthen the agricultural sector. Social network analysis (SNA) and machine learning (ML) techniques make it feasible to assess the service performance in China's SCS by using indicators such as group types and structure features. In this study, SNA and a clustering algorithm were employed to categorize service group types of sci-tech commissioners. By comparing the accuracy of different classification algorithms in predicting the clustering results, LightGBM algorithm was finally select to determine the clustering features of sci-tech commissioners and establish an interpretable ML model. Then, the SHAP was used to algorithm to analyze influences affecting service performance. Results show that the service forms of sci-tech commissioners are group-oriented, and that group types include small groups of young commissioners with close cooperation, larger groups of young and middle-aged commissioners, small groups of middle-aged and old commissioners with close cooperation, and isolated points of highly-influential commissioners. Furthermore, while group size is not the determinant of a commissioner's average performance, group structure and coordination ability were found to be more critical. Moreover, while differences in distinct types of service performance are caused by various factors, but good group structures and extensive social contacts are essential for high service performance.

The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review

BACKGROUND

Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM.

OBJECTIVE

Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field.

METHODS

Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data.

RESULTS

A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills.

CONCLUSIONS

LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.

The impact of public leadership on collaborative administration and public health delivery

BACKGROUND

This research depicts the linkage of public leadership on public health delivery (PHD) and collaborative administration. The research is also focused to examine the effect of public leadership on public health delivery through the intervening variable of collaborative administration by using both social information processing theory and collaboration theory.

METHODS

This research is based on quantitative method. Data was collected from 464 public hospital administration in the context of Pakistan. This study evaluated data using SPSS, AMOS, and PROCESS Macro.

RESULTS

Public leadership has a positive profound effect on public health delivery and collaborative administration, and that collaborative administration significantly promotes public health delivery. The outcomes also exposed that public leadership has substantial influence on public health delivery through intervening collaborative administration.

CONCLUSIONS

Whilst public leadership demonstrated positive outcomes on public health delivery and collaborative administration, there is a need for more rigor studies on collaborative governance leadership, collaborative ethics and collaborative norms in the public health service.

Urban residents' self-rescue in response to public health emergencies in China: a qualitative study

BACKGROUND

The abject uncertainty and unpredictability of public health emergencies have plagued various countries. Global health governance and international communities are facing long-term and arduous challenges. The self-rescue ability of individuals in a public emergency may be the most powerful trait to improve the survival rate outside the hospital. The study explores the cognitive ability and attitudes of urban residents in China towards self-rescue in response to public health emergencies. It provides appropriate evidence for improving the self-rescue ability of urban residents in China.

METHODS

Sixteen urban residents were selected using the purposive sampling method for semi-structured interviews. Theme analysis was used to collate and analyse the interview data.

RESULTS

Two themes and five sub-themes were analysed. The two themes included cognition and attitude of Chinese urban residents for self-rescue in an emergency. Urban residents believed that their knowledge and skills for self-rescue in an emergency were low. The ability for emergency self-rescue is affected by multiple factors, with relatively limited options for improvement. Nonetheless, the respondents expressed a desire to accept interventions under psychological crisis and a strong willingness to acquire knowledge and skills required for emergency self-rescue.

CONCLUSION

This study investigated the perceptions and attitudes of Chinese urban residents towards emergency self-rescue. The results support enhanced ability of urban residents to respond to public health emergencies, thereby diminishing the negative outcomes. The findings suggest the need for strategies to address the factors affecting emergency self-rescue.