The agricultural sector is vital for food production, economic stability, and environmental sustainability. However, it also poses significant occupational health and safety challenges due to factors such as heavy machinery usage, exposure to chemicals, and challenging environmental conditions. This cross-sectional study involved 366 farmers affiliated with the Yozgat Chamber of Agriculture in Turkey. Data on demographic characteristics, occupational factors, and work accidents were collected with interview technique between May and August 2023. While descriptive data were expressed as categories with numbers and percentages, chi-square analysis was used to compare the obtained answers with the status of having or not having a work accident. Binary logistic regression analysis was used to estimate the effect of independent variables on the probability of having a work accident. The study revealed that 33.0% of farmers experienced work accidents in the last year. Most accidents occurred during work shifts (28.8%) and in the fall season (34.8%). Factors contributing to accidents included lack of personal protective equipment (51.5%) and transportation-related issues (36.4%). Logistic regression analysis indicated that working more than 8 h daily, working 5 days or more per week, and using bus/minibus transportation significantly increased the likelihood of experiencing work accidents. Findings underscore the importance of implementing effective occupational health and safety measures, including proper training, provision of personal protective equipment, and improving transportation safety. Addressing these issues can enhance the well-being of agricultural workers and contribute to a safer working environment in the sector.

Background: Seasonal agricultural workers working and living in inappropriate sanitary conditions are at great risk for public health. This study aimed to determine the relationships between the sociodemographic variables and life satisfaction of seasonal agricultural workers, and their knowledge, risk perception, and protective behaviors about the COVID-19 pandemic.Methods: This is a cross-sectional study, that included agricultural workers who are 18 years of age or older and worked seasonally in Yozgat, Turkey, during the period between August 2020 and October 2020. The well-being level was measured using the Personal Wellbeing Index-Adult form (PWIA). The data were collected using the face-to-face survey method and with 739 workers who voluntarily participated in the research.Results: All participants disclosed having insufficient information about Covid-19 and indicated their peers and television as their sources of information. The vast majority of the workers stated that they complied with the mask mandates, social distancing, and hand hygiene. No correlations were found between knowledge, attitudes, and behaviors about Covid-19 and the level of wellbeing. The mean PWIA score of the workers was low (53.7) while they were mostly satisfied with their personal relationships (96.6) and health (76.1). The multivariable linear regression analysis revealed that being male (β = 0.245) and not having an ongoing health issue (β = 0.689) were associated with more PWIA; on the other hand, having more children (β = -0.52) was related to less PWIA.Conclusions: The well-being level of seasonal workers was lower while it was not associated with knowledge, attitudes, and behaviors about Covid-19.

Background: The aim of this study is to examine the effects of working environment and demographic variables on the level of work limitation in a university hospital. Methods: The study is cross-sectional and was conducted in 2022 among employees of a university hospital. 254 people voluntarily participated in the study. Data were collected by applying the sociodemographic data form, the Work Limitation Questionnaire (WLQ), and the Work Environment Scale (WES). Institutional permission and ethical approval were obtained for the study. In the analysis of the data, t-test, ANOVA, and linear regression (LR) were used. Results: The WLQ score average of hospital staff was low. According to LR analysis, the factors affecting the level of work limitation of hospital staff; worsening perception of health status, being a doctor, decreased income level, increased working time in the institution, and age reduction. It was determined that 32.8% of the change in the WLQ score was related to these factors. While in the univariate tests, the mean of work limitation was found to be significant by getting occupational health safety training, having health problems due to the work done, and taking leave due to work accidents, in the multivariable LR analysis, these factors were insignificant. Conclusions: As the working environment gets worse, the level of work limitation increases. It is recommended that hospital managers make the working environment better and safer, and make arrangements and programs to increase personnel satisfaction.

Dental trauma represents a common emergency condition requiring rapid and accurate guidance to prevent permanent damage. In urgent scenarios where access to dental professionals may be delayed, large language models (LLMs) have the potential to provide patients with timely and relevant information. The aim of this study was to comparatively evaluate the dentist rated performance of five widely used LLMs in answering frequently asked questions related to dental trauma emergencies across three predefined components: accuracy, comprehensiveness, and clinical applicability. Based on the guidelines of the International Association of Dental Traumatology (IADT), the ToothSOS application, and frequently asked patient questions, 27 open-ended questions in Turkish were prepared and divided into five clinical subcategories: avulsion, post-replantation care, luxation, fractures, and other traumas. The questions were posed to the models ChatGPT-4o, Claude 3.5, DeepSeek, Microsoft Copilot, and Gemini 2.0 Flash. Twenty experienced dentists evaluated the responses using a 5-point Likert scale, with the evaluators jointly considering three predefined components: accuracy, comprehensiveness, and clinical applicability. A total of 2,700 individual ratings were analyzed using the Friedman test, Bonferroni-corrected Wilcoxon test, and Intraclass Correlation Coefficient (ICC). A significant difference in overall performance was observed among the models (p < 0.001). ChatGPT-4o achieved the highest mean score (4.63 ± 0.57), whereas Gemini received the lowest score (3.87 ± 0.91). Claude, DeepSeek, and Copilot demonstrated similar, moderate performance, ranging approximately between 4.1 and 4.3. Median values were 5 (IQR 4–5) for ChatGPT-4o and Claude and 4 (IQR 3–5) for the other models, indicating that the responses of ChatGPT-4o and Claude were rated more consistently high. Within each model, no statistically significant differences were observed in mean Likert scores across clinical subcategories (p > 0.05). Although LLMs may not be able to replace professional clinical examination, they may serve as a rapid supportive source of patient-oriented information in dental trauma emergencies where access to a dentist is limited. However, model dependent differences, highlight the need for regular verification before patient facing use. The online version contains supplementary material available at 10.1186/s12903-026-08037-8.

Objective: Increasing violence against healthcare workers poses significant challenges in service delivery and workforce retention. This study examines the phenomenon of violence against healthcare professionals by analyzing Twitter discussions as a window to public perceptions and attitudes. Material and Methods: This research used qualitative content analysis approach. The study aimed to identify common themes, trends and user perspectives on violence against healthcare professionals. Through analysis, the study identified user profiles, trending topics, and violence categories. Results: The findings revealed that tweets discussing violence in healthcare came primarily from users associated with the medical professions, healthcare institutions, patients and their relatives. The study identified recurring themes such as fatal attacks against healthcare professionals, non-fatal physical attacks, and incidents of psychological abuse. Tweets often criticized the inadequacy of legal measures and social indifference to healthcare violence. Tags and key terms such as #violence, #doctor, #nurse, #threat and #attack were common in these discussions. Conclusion: This research contributes to the limited literature on societal perspectives of violence in health care. It highlights the need for comprehensive data to understand the multifaceted nature of this problem, revealing the diversity of events and user responses. The study highlights the role of social media, particularly Twitter, in facilitating discourse about violence and its consequences in healthcare. These insights can help design effective interventions and policies to address and reduce violence against healthcare workers, and ultimately improve the work environment and quality of patient care.