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Sharing project information is critical for the success of medical crowdfunding campaigns. However, few users share medical crowdfunding projects on their social networks, and the sharing behavior of medical crowdfunding projects on social networking sites has not been well studied. Therefore, this study explored the factors and potential mechanisms influencing users’ sharing behaviors on networking sites.

A research model was developed based on the attribution-affect model of helping and social capital theory. Data were collected using a longitudinal survey. Partial least squares structural equation modeling was used to analyze the collected data. We conducted post hoc analyses to validate the results of the quantitative analysis.

The analysis results verified the effects of perceived external attribution, perceived uncontrollable attributions, and perceived unstable attributions on sympathy and identified the effect of sympathy and social characteristics of medical crowdfunding users on sharing behavior.

This research provides a comprehensive theoretical understanding of users’ sharing behavior characteristics and provides implications for enhancing the efficiency of medical crowdfunding activities.

The library is one of the critical foundations of informing and educating the public, and it should fulfil its function in the community, which is disseminating knowledge effectively. The library must have a dynamic space and an appropriate atmosphere to do this. Also, librarians should assist clients outside of stressful work environments. This study aimed to examine the association between organizational climate and job burnout in the hospital and college libraries of Shahid Beheshti University of Medical Sciences.

This study is a cross-sectional survey. The population includes all librarians working in the college and hospital libraries of Shahid Beheshti University of Medical Sciences. Two questionnaires, including organizational climate “ClimateQUAL” and job burnout “Maslach and Jackson”, were applied to collect data. It was hypothesized that there is a correlation between the organizational climate and the burnout of librarians.

The findings revealed that the organizational climate and job burnout are not in a favourable situation, with average scores of 4.70 and 2.50, respectively. Furthermore, there is a significant statistical relationship between organizational climate and job burnout (p-value<0.001).

Knowledge of the significant relationship between the organizational climate and job burnout in medical libraries may encourage policymakers to make more efforts to provide their staff with a better climate and less burnout. Regarding that it is crucial to enhance the existing condition in medical libraries, the findings of the present study suggest that policymakers should be conscious of improving the current condition, which could lead to promoting patrons’ satisfaction and productivity in the mentioned libraries.

Applying the value-attitude-behavior (VAB) model, this study investigated how perceived utilitarian and hedonic values (i.e. novelty and emotion) affect individuals' attitudes toward medical hotels and how these attitudes, in turn, influence their intentions to stay at medical hotels. The current study also explored the moderating impact of overall health status on the relationships between perceived utilitarian, novelty and emotional values and attitudes toward medical hotels.

The data collected from 351 individuals who spent a night in a hospital to undergo medical treatment was used in conducting structural equation modeling to evaluate the research model and test the study hypotheses.

The study results revealed that perceived utilitarian, novelty and emotional values exerted a positive influence on individuals’ attitudes toward medical hotels, consequently enhancing their intention to stay. Additionally, significant moderating impacts of overall health status on the associations between perceived utilitarian, novelty and emotional values and attitudes toward medical hotels were observed.

The study findings provide useful guidance for professionals, such as operators, marketers and managers in medical hotels. These insights can improve operational and marketing strategies, benefiting both the industry and healthcare accommodation seekers.

This study is among the first to empirically assess a theoretical model that explored the influence of individuals' value perceptions within the realm of medical hotel establishments.

This study aims to explore the consumer perceptions of cognition and intention to visit pilot zone of international medical tourism as emerging, developed medical tourism destinations.

Using a survey-based quantitative method, based on a survey of 439 tourists who have cross-border travel experience, the partial least squares approach was performed to test the hypotheses.

The results show that internal factors had a stronger influence on destination image compared to external factors. Among different factors, preferential policies had the greatest impact on intention to visit. Perceived quality had a stronger effect on intention to visit than preference. Geographical distance had a varied effect, with those furthest away in Northeast China showing greater intention to visit compared to closer regions.

This study explores the impact of multidimensional destination perception on medical tourists’ behavioural intention in emerging destinations by integrating the push-pull theory and theory of planned behaviour and tests how geographical distance affects intention to visit emerging destinations. Using China international medical tourism pilot area as a typical case of medical tourism emerging destinations for empirical analysis. This research offers guidance for branding and marketing strategies, contributes to a deeper understanding of medical tourists’ destination choices, enriches the theoretical explanation of emerging destination choice in medical tourism and provides valuable insights for destination recovery.

Artificial intelligence (AI) is a growing paradigm and has made considerable changes in many fields of study, including medical education. However, more investigations are needed to successfully adopt AI in medical education. The purpose of this study was identify the determinant factors in adopting AI-driven technology in medical education.

This was a descriptive-analytical study in which 163 faculty members from Tabriz University of Medical Sciences were randomly selected by nonprobability sampling technique method. The faculty members’ intention concerning the adoption of AI was assessed by the conceptual path model of task-technology fit (TTF).

According to the findings, “technology characteristics,” “task characteristics” and “TTF” showed direct and significant effects on AI adoption in medical education. Moreover, the results showed that the TTF was an appropriate model to explain faculty members’ intentions for adopting AI. The valid proposed model explained 37% of the variance in faulty members’ intentions to adopt AI.

By presenting a conceptual model, the authors were able to examine faculty members’ intentions and identify the key determining factors in adopting AI in education. The model can help the authorities and policymakers facilitate the adoption of AI in medical education. The findings contribute to the design and implementation of AI-driven technology in education.

The finding of this study should be considered when successful implementation of AI in education is in progress.

A new triage method, MBCE (Medical Bio Social Ethics), is presented with social justice, bio, and medical ethics for critical resource distribution during a pandemic. Ethical triage is a complex and challenging process that requires careful consideration of medical, social, cultural, and ethical factors to guide the decision-making process and ensure fair and transparent allocation of resources. When assigning priorities to patients, a clinician would evaluate each patient’s medical condition, age, comorbidities, and prognosis, as well as their cultural and social background and ethical factors.

A statistical analysis shows no interactions among the ethical triage factors. It implies the ethical components have no moderation effect; hence, each is independent. The result also points out that medical and bioethics may have an affinity for interactions. In such cases, there seem to be some ethical factors related to bio and medical ethics that are correlated. Therefore, the triage team should be careful in evaluating patient cases. The algorithm is explained with case histories of the selected patient. A group of triage nurses and general medical practitioners assists with the triage.

The MBCE triage algorithm aims to allocate scarce resources fairly and equitably. Another ethical principle in this triage algorithm is the principle of utility. In a pandemic, the principle of utility may require prioritizing patients with a higher likelihood of survival or requiring less medical care. The research presents a sensitivity analysis of a patient’s triage score to show the algorithm’s robustness. A weighted score of ethical factors combined with an assessment of triage factors combines multiple objectives to assign a fair triage score. These distinctive features of the algorithm are reasonably easy to implement and a new direction for the unbiased triage principle.

The idea is to make decisions about distributing and using scarce medical resources. Triage algorithms raise ethical issues, such as discrimination and justice, guiding medical ethics in treating patients with terminal diseases or comorbidity. One of the main ethical principles in triage algorithms is the principle of distributive justice.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

In Saudi Arabia, quality management receives a significant amount of attention in higher education. In medical education, specifically, Saudi colleges have been fully engaged with quality assurance and accreditation since 1992, under the supervision of the Saudi Council for Health Specialties. Along with the quality standards determined by accreditation agencies, the perspective of employers on the quality of learning and teaching needs to be acknowledged. The needs of medical employers can be translated into quality standards for medical education to help overcome the perceived deficiencies which lead to poorly equipped graduates. This chapter explores how employers conceptualize quality in medical education with an emphasis on learning and teaching and employers’ perspectives on the quality attributes of medical graduates. This chapter is based on interviews with 14 medical employers in Saudi Arabia. From the employers’ perspective, a high-quality medical education is marked by high quality educational systems, curricula, faculty members, and medical training. Additionally, medical graduates must attain a balance of soft skills, practical and clinical skills, and theoretical medical knowledge. Understanding employers’ perspectives on quality in medical education will complement our existing understanding of quality in medical education.