BACKGROUND: Patients with complex medical comorbidities travel for protracted periods to remote destinations, often with limited access to medical care. Few descriptions are available of their preexisting health burden. This study aimed to characterize preexisting medical conditions and medications of travelers seeking pre-travel health advice at a specialized travel medicine clinic.
METHODS: Records of travelers attending the Galway Tropical Medical Bureau clinic between 2008 and 2014 were examined and information relating to past medical history was entered into a database. Data were recorded only where the traveler had a documented medical history and/or was taking medications.
RESULTS: Of the 4,817 records available, 56% had a documented medical history and 24% listed medications. The majority of travelers with preexisting conditions were female. The mean age of the cohort was 31.68 years. The mean period remaining before the planned trip was 40 days. Southeast Asia was the most popular single destination, and 17% of travelers with medical conditions were traveling alone. The most frequently reported conditions were allergies (20%), insect bite sensitivity (15%), asthma (11%), psychiatric conditions (4%), and hypertension (3%). Of the 30 diabetic travelers, 14 required insulin; 4.5% of travelers were taking immunosuppressant drugs, including corticosteroids. Half of the female travelers were taking the oral contraceptive pill while 11 travelers were pregnant at the time of their pre-travel consultation.
CONCLUSIONS: This study provides an insight into the medical profile of travelers attending a travel health clinic. The diverse range of diseases reported highlights the importance of educating physicians and nurses about the specific travel health risks associated with particular conditions. Knowledge of the effects of travel on underlying medical conditions will inform the pre-travel health consultation.
In light of the increasing importance digital economy, the significance of computational thinking has grown exponentially, becoming imperative in both workplace and academic settings such as universities. This article addresses the critical need to comprehend the factors influencing the acceptance of computational thinking. The dataset introduces an extensive questionnaire comprising five constructs and 25 items, rooted in the extended Technology Acceptance Model. Notably, the model incorporates facilitating conditions and subjective norm, providing a comprehensive framework for understanding acceptance. Data collection involved 132 undergraduate university students sampled through purposive sampling, specifically targeting courses with a focus on computational thinking. The resulting dataset serves as a valuable resource for future research, offering detailed insights into the factors determining the acceptance of technology in educational contexts beyond mere thinking skills. Given the scarcity of research on technology acceptance in developing nations, this dataset holds particular significance, serving as a foundation for potential cross-cultural comparisons. The dataset contributes to the field by presenting a robust acceptance model, explaining 74.2 per cent of the variance in behavioural intention, 60.2 per cent in perceived usefulness, and 56.1 per cent in perceived ease of use. This high explanatory power positions the dataset as a superior resource for replication, benchmarking, and broader applicability in diverse contexts, thereby enhancing the understanding of computational thinking acceptance across different populations and settings. This dataset stands among the pioneering efforts to assess the novel covariance-based structural equation model algorithm within SmartPLS 4, presenting a valuable resource for future research employing the same mechanism.