Modelling the Predictors of Mobile Health (mHealth) Adoption among Healthcare Professionals in Low-Resource Environments.

This study was conducted with objectives to measure and validate the unified theory of the acceptance and use of technology (UTAUT) model as well as to identify the predictors of mobile health (mHealth) technology adoption among healthcare professionals in limited-resource settings. A cross-sectional survey was conducted at the six public and private hospitals in the two districts (Lodhran and Multan) of Punjab, Pakistan. The participants of the study comprised healthcare professionals (registered doctors and nurses) working in the participating hospitals. The findings of the seven-factor measurement model showed that behavioral intention (BI) to mHealth adoption is significantly influenced by performance expectancy (ß = 0.504, CR = 5.064, p < 0.05) and self-concept (ß = 0.860, CR = 5.968, p < 0.05) about mHealth technologies. The findings of the structural equation model (SEM) showed that the model is acceptable (χ2 (df = 259) = 3.207; p = 0.000; CFI = 0.891, IFI = 0.892, TLI = 0.874, RMSEA = 0.084). This study suggests that the adoption of mHealth can significantly help in improving people's access to quality healthcare resources and services as well as help in reducing costs and improving healthcare services. This study is significant in terms of identifying the predictors that play a determining role in the adoption of mHealth among healthcare professionals. This study presents an evidence-based model that provides an insight to policymakers, health organizations, governments, and political leaders in terms of facilitating, promoting, and implementing mHealth adoption plans in low-resource settings, which can significantly reduce health disparities and have a direct impact on health promotion.

An overview of the capabilities of ChatGPT for medical writing and its implications for academic integrity.

The artificial intelligence (AI) tool ChatGPT, which is based on a large language model (LLM), is gaining popularity in academic institutions, notably in the medical field. This article provides a brief overview of the capabilities of ChatGPT for medical writing and its implications for academic integrity. It provides a list of AI generative tools, common use of AI generative tools for medical writing, and provides a list of AI generative text detection tools. It also provides recommendations for policymakers, information professionals, and medical faculty for the constructive use of AI generative tools and related technology. It also highlights the role of health sciences librarians and educators in protecting students from generating text through ChatGPT in their academic work.

Academic integrity among medical students and postgraduate trainees in the teaching hospitals of South Punjab Pakistan.

This study investigated the topic of the academic integrity among medical students and postgraduate trainees in the teaching hospitals of South Punjab, Pakistan. A cross-sectional survey was conducted involving 318 medical students and postgraduate trainees of teaching hospitals. The results found that medical students of pre-clinical years engaged in unethical behaviour, that is, exam cheating and plagiarism to cope with internal and external evaluation and the range of subjects needed to be studied. For postgraduate trainees, results showed trainees unintentionally engaged in the practice of plagiarism due to lack of understanding about what constitutes plagiarism, coupled with externally perceived pressures associated with expectations of research publication, promotions and tenured positions. To address these concerns, it is recommended that information literacy sessions for undergraduate and postgraduate medical students on plagiarism prevention and ethical practice be designed and facilitated by medical librarians in collaboration of faculty members.