BACKGROUND: Human mobility data have been used as a potential novel data source to guide policies and response planning during the COVID-19 global pandemic. The COVID-19 Mobility Data Network (CMDN) facilitated the use of human mobility data around the world. Both researchers and policy makers assumed that mobility data would provide insights to help policy makers and response planners. However, evidence that human mobility data were operationally useful and provided added value for public health response planners remains largely unknown.
OBJECTIVE: This exploratory study focuses on advancing the understanding of the use of human mobility data during the early phase of the COVID-19 pandemic. The study explored how researchers and practitioners around the world used these data in response planning and policy making, focusing on processing data and human factors enabling or hindering use of the data.
METHODS: Our project was based on phenomenology and used an inductive approach to thematic analysis. Transcripts were open-coded to create the codebook that was then applied by 2 team members who blind-coded all transcripts. Consensus coding was used for coding discrepancies.
RESULTS: Interviews were conducted with 45 individuals during the early period of the COVID-19 pandemic. Although some teams used mobility data for response planning, few were able to describe their uses in policy making, and there were no standardized ways that teams used mobility data. Mobility data played a larger role in providing situational awareness for government partners, helping to understand where people were moving in relation to the spread of COVID-19 variants and reactions to stay-at-home orders. Interviewees who felt they were more successful using mobility data often cited an individual who was able to answer general questions about mobility data; provide interactive feedback on results; and enable a 2-way communication exchange about data, meaning, value, and potential use.
CONCLUSIONS: Human mobility data were used as a novel data source in the COVID-19 pandemic by a network of academic researchers and practitioners using privacy-preserving and anonymized mobility data. This study reflects the processes in analyzing and communicating human mobility data, as well as how these data were used in response planning and how the data were intended for use in policy making. The study reveals several valuable use cases. Ultimately, the role of a data translator was crucial in understanding the complexities of this novel data source. With this role, teams were able to adapt workflows, visualizations, and reports to align with end users and decision makers while communicating this information meaningfully to address the goals of responders and policy makers.

OBJECTIVE: To explore the experiences of clinician and management stakeholders involved in a rural/metropolitan collaborative mental health disaster response to the 2019-2020 Black Summer bushfires in the Snowy Valleys region of southern New South Wales (NSW), Australia.
METHODS: A mental health and drug health service in the Snowy Valleys region of rural NSW in collaboration with a mental health service from metropolitan Sydney, NSW.
METHODS: Mental health clinicians and managers from a rural health district (n = 6) and a metropolitan health district (n = 8) involved in a collaborative disaster response to the 2019-2020 Black Summer bushfire disaster in the Snowy Valleys region of southern NSW, Australia.
METHODS: An interpretive qualitative study design using semi-structured individual interviews, with transcripts analysed using Reflexive Thematic Analysis.
RESULTS: Thematic findings on participant experiences are presented under three organising constructs of before (stepping up and jumping right in), during (finding a rhythm of working together), and after (profound personal and professional impacts) the mental health disaster response.
CONCLUSIONS: Participant experiences had shared and distinct components before, during and after the mental health disaster response, culminating in profound personal and professional impacts. Findings highlight positive aspects and challenges for clinicians participating in a rural/metropolitan collaborative mental health disaster response. The findings of this study contribute new knowledge about experiences of mental health clinicians participating in a disaster response after bushfires, from dual perspectives of members of a bushfire-affected community and those responding from outside a bushfire-affected community, which may inform ongoing planning of responses to disaster in Australia.

BACKGROUND: Turkey has faced a notable escalation in earthquake disasters in the last two decades. Despite initiating a health and disaster management system, nurses\' pivotal roles and experiences in handling such crises have been disregarded.
OBJECTIVE: This qualitative study analyzed nurses\' experiences before, during, and after deployment in response to the 2023 Turkey earthquakes to enhance disaster-response efforts.
METHODS: This descriptive qualitative study was conducted between March and May 2023 using semistructured interviews with 15 nurses purposively  sampled among those who volunteered to work in the earthquake zone. The MIRACLE and COREQ guidelines were followed for reporting qualitative research.
RESULTS: The analysis exposed five main themes for pre-tasking: moral obligation, motivation, insufficient experience, balancing responsibilities, and preparation challenges. The peri-task themes include responsibilities, skills, bravery and characteristics, workload management, teamwork, and outcomes. Post-tasking has three themes: competence assessment, career goals and aspirations, and support. Training and coping with anxiety and stress are common themes for all phases.
CONCLUSIONS: Disaster relief requires a comprehensive and coordinated response from healthcare organizations, government agencies, and support systems. Providing adequate training, ensuring safety protocols, offering mental health support, and fostering a fair and supportive work environment are crucial steps in mitigating the adverse effects on nurses and, by extension, the patient care process in earthquake-affected areas.
CONCLUSIONS: Nurse training in disaster preparedness should cover various response methods and involve multiple disciplines. Managers can help by arranging drills, simulations, online courses, and workshops and promoting partnerships for improved collaboration. Psychological support should be included to address emotional challenges. Regularly updating response policies based on past experiences is crucial for preparedness and efficiency.

OBJECTIVE: The objective of this study was to offer a comprehensive synthesis of the existing Key performance indicators (KPIs) used in the evaluation of the pre-Hospital response to disasters and mass casualty incidents (MCIs).
METHODS: At the end of December 2022 a scoping review has been performed on PubMed, Scopus, Embase, and Medline to identify articles describing the use of KPIs to assess the performance of first responders during the prehospital phase of an MCI (real or simulated). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, fourteen articles were included in the analysis.
RESULTS: Eleven articles applied indicators in exercises and/or simulations. Two articles proposed new KPIs, and one used KPIs for developing a model for benchmarking pre-Hospital response. All articles analyzed quantitative indicators of time, whereas two studied indicators of structure, of process, and of outcome as well.
CONCLUSIONS: The findings from this review emphasize the need for employing common terminology and using uniformed data collection tools, if obtaining standardized evaluation method is the goal to be achieved.

Objectives: This study examined the actual conditions of service provision to foreign residents (FRs) addressing prevention of lifestyle-related diseases (LRDs) and disaster preparedness/disaster responses (DPRs) in Japanese municipalities. Materials and Methods: A cross-sectional study was performed using a self-administered questionnaire with representatives of public health nurses in each municipality in Japan from December 2021 to January 2022. Results: Services considering FRs are more likely to be implemented in cities than in towns, and in municipalities where FRs account for ≥2.2% of the population (proportion of FRs in the Japanese population at the time of the study) than in those with fewer FRs. Cities have larger populations and greater financial resources than towns. Factors associated with the implementation of services and measures necessary for providing services to FRs were the classification of the municipality as a city, a high percentage of FRs, and large variation in corresponding nationalities/countries of origin. Conclusions: Cross-disciplinary efforts and collaborations need to be strengthened to share available resources within local governments and experiences in providing services for FRs in other divisions/sections, rather than considering only how to provide services for FRs in the public health division/section.

In this study, we address the critical need for enhanced situational awareness and victim detection capabilities in Search and Rescue (SAR) operations amidst disasters. Traditional unmanned ground vehicles (UGVs) often struggle in such chaotic environments due to their limited manoeuvrability and the challenge of distinguishing victims from debris. Recognising these gaps, our research introduces a novel technological framework that integrates advanced gesture-recognition with cutting-edge deep learning for camera-based victim identification, specifically designed to empower UGVs in disaster scenarios. At the core of our methodology is the development and implementation of the Meerkat Optimization Algorithm-Stacked Convolutional Neural Network-Bi-Long Short Term Memory-Gated Recurrent Unit (MOA-SConv-Bi-LSTM-GRU) model, which sets a new benchmark for hand gesture detection with its remarkable performance metrics: accuracy, precision, recall, and F1-score all approximately 0.9866. This model enables intuitive, real-time control of UGVs through hand gestures, allowing for precise navigation in confined and obstacle-ridden spaces, which is vital for effective SAR operations. Furthermore, we leverage the capabilities of the latest YOLOv8 deep learning model, trained on specialised datasets to accurately detect human victims under a wide range of challenging conditions, such as varying occlusions, lighting, and perspectives. Our comprehensive testing in simulated emergency scenarios validates the effectiveness of our integrated approach. The system demonstrated exceptional proficiency in navigating through obstructions and rapidly locating victims, even in environments with visual impairments like smoke, clutter, and poor lighting. Our study not only highlights the critical gaps in current SAR response capabilities but also offers a pioneering solution through a synergistic blend of gesture-based control, deep learning, and purpose-built robotics. The key findings underscore the potential of our integrated technological framework to significantly enhance UGV performance in disaster scenarios, thereby optimising life-saving outcomes when time is of the essence. This research paves the way for future advancements in SAR technology, with the promise of more efficient and reliable rescue operations in the face of disaster.

Breakthroughs in international biomedical science circa 1900 meant that plague could be contained through strict quarantine regulations. These measures were successfully deployed with help from local governments during outbreaks of pneumonic plague in Manchuria (1910-11), Shanxi (1918), and elsewhere in North China. This containment shows the effectiveness of uniting international knowledge and local cooperation in disaster response. Yet, in later outbreaks in similar locations, control measures identical to those instituted a decade earlier were rejected, and plague spread largely unchecked. Historical case studies of the control and spread of infectious disease in North China reveal the complexities of the relationship between global knowledge and its broader, local integration, variation in what constitutes effective \'local\' cooperation in adopting international knowledge, and the paramount importance of the locality to the landscape of disaster response. History can reveal critical issues in localisation of disaster response still salient today.
国際的な生物医学の進歩により、1900 年頃には厳格な検疫規制によってペストを封じ込めることができるようになった。これらの対策は、満州(1910 ~ 1911 年)、山西省(1918 年)、および中国北部のその他の地域での風疫病の発生時に、地方自治体の援助により成功裡に実施された。この封じ込めは、災害対応において国際的な知識と地域の協力を統合することの有効性を示している。しかし、1921年と1928年に同様の場所で大流行があった際、わずか10年前に導入されたものと同一の制御措置は拒否され、ペストの蔓延にはほとんど歯止めがかからなかった。中国北部における感染症の制御と蔓延に関するこれらの歴史的な事例研究は、世界的な知識とその知識の地元での受け入れとの関係の複雑さ、国際知識を採用する際の効果的な「地元」協力を構成するものの多様性、そして災害対応の状況における地元の決定的な重要性を明らかにしている。今日でも顕著に見られる災害対応の地域化における重大な課題が、歴史によって明らかにされることもあるのだ。.
1900 年左右国际生物医学科学的突破意味着可以通过严格的检疫规定来控制鼠疫。在满洲(1910‐1911 年)、山西(1918 年)和华北其他地区爆发鼠疫期间，在当地政府的帮助下，这些措施得到了成功实施。这次遏制行动体现了在灾难应对中联合国际知识和当地合作的有效性。然而，1921年和1928年，在类似地点爆发疫情时，与十年前实施的控制措施相同的措施被拒绝，鼠疫的传播基本上不受控制。这些有关华北地区传染病控制与传播的历史案例研究，揭示了全球知识与当地对这些知识的接受之间关系的复杂性，在采用国际知识时有效的“当地”合作的构成因素的多样性，以及当地情况对灾害应对形势的关键性。历史可以揭示出灾难响应本地化中至今仍突出的关键问题。.

Smooth interaction with a disaster-affected community can create and strengthen its social capital, leading to greater effectiveness in the provision of successful post-disaster recovery aid. To understand the relationship between the types of interaction, the strength of social capital generated, and the provision of successful post-disaster recovery aid, intricate ethnographic qualitative research is required, but it is likely to remain illustrative because it is based, at least to some degree, on the researcher\'s intuition. This paper thus offers an innovative research method employing a quantitative artificial intelligence (AI)-based language model, which allows researchers to re-examine data, thereby validating the findings of the qualitative research, and to glean additional insights that might otherwise have been missed. This paper argues that well-connected personnel and religiously-based communal activities help to enhance social capital by bonding within a community and linking to outside agencies and that mixed methods, based on the AI-based language model, effectively strengthen text-based qualitative research.
災害の影響を受けたコミュニティとの円滑な交流は、コミュニティの社会資本を構築および強化することができ、災害後の復興支援をより効果的に提供することにつながる。相互作用の種類、生成される社会資本の強さ、および成功した災害復興支援の提供の関係を理解するには、複雑な民族誌的かつ質的研究が必要であるが、それは少なくともある程度は研究者の直感に基づいているため、例示にとどまる可能性が高い。このような研究に次元を加えて強化するために、この論文では、定量的な AI ベースの言語モデルを使用した革新的研究手法を提示する。本モデルを使用することで、質的研究のデータを再調査して結果を検証し、見逃されていた可能性のあるその他の洞察の収集が可能となる。本論文では、人間関係の良好な人材と宗教に基づいた共同体活動が、コミュニティ内での絆や外部機関とのつながりによって社会資本の強化に役立つと論じている。また、AI ベースの言語モデルに基づく混合手法により、テキストベースの質的研究を効果的に強化できると論じている。.
与受灾社区的顺畅互动可创造和加强社区的社会资本，从而更有效地提供灾后恢复援助。为了理解互动类型所产生的社会资本强度以及成功的灾后恢复援助之间的关系，需要进行复杂的民族志定性研究，但它可能仍具有说明性，因为它至少在某种程度上基于研究人员的直觉。为了增加维度并加强此类研究，本文提出了一种采用基于定量人工智能的语言模型的创新研究方法。该模型允许研究人员重新审查数据，从而验证定性研究的结果，并收集定性研究可能错过的额外见解。本文认为，人脉广泛的人员和以基于宗教的社区活动有助于通过社区内的联系和与外部机构的联系来增强社会资本。它还认为，基于人工智能语言模型的混合方法能有效地加强基于文本的定性研究。.

BACKGROUND: The presence of nurses during disasters can lead to many consequences. Understanding the nurses\' experiences of these consequences can provide valuable insights. Therefore, this study was conducted to explore the experiences of Iranian nurses regarding the consequences they faced when being present during disasters.
METHODS: This qualitative descriptive study employed a content analysis approach to examine the topic at hand. 20 nurses working in the emergency units of Kerman hospitals were selected through purposive sampling. In-depth semi-structured interviews were conducted to collect the data. The data were analyzed using qualitative content analysis proposed by Graneheim and Lundman. MAXQDA 10 was used to manage data.
RESULTS: After analysis of the interviews, two main categories were identified: overlapping of job frustrations and acquiring experience in difficult conditions. The main category of acquiring experience in difficult conditions comprised the following subcategories: improved quality of care, experience and dedication in fulfilling the role, reduced stress, occupational motivation and enthusiasm, increased self-confidence, and improved social status. On the other hand, the category of overlapping of job frustrations also consisted of the subcategories of physical strength deterioration, psychological and emotional distress, discontinued from supports, feelings of hopelessness, increased exposure to violence and aggression, and occurrence of family problems.
CONCLUSIONS: Being present during disasters and obtaining the necessary competencies can have positive consequences that serve as turning points in the personal and professional growth of nurses. Additionally, favorable outcomes can help nurses cope with adverse circumstances. Managers can utilize the findings of this study to develop strategies aimed at reducing negative outcomes and enhancing positive ones among nurses during disasters.

As the number of confirmed COVID-19 cases nears 27 million, there is a rush to answer (what next) and a rush to act (to solve the immediate problems of COVID-19). This paper discusses, with a specific focus on urban areas in the global South, what is missing to date from this response. That includes an identification of things that there are too much of, things that are not being done at all, and things that are unbalanced. There has been an enormous upsurge of academic research papers and opinions on COVID-19. \"Technological\" and \"scientific\" solutions tend to overshadow other approaches, even if people know that \"social is important\". Based on our analysis to date, our primary concern is that there is too little understanding about the importance of building dialogue, exploring collaboration and co-producing solutions. There is too little understanding as to why social and cultural responses are important, and how the recognition that they are important can be actioned.