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Qualitative Evaluation of mHealth Implementation for Infectious Disease Care in Low- and Middle-Income Countries: Narrative Review

Qualitative Evaluation of mHealth Implementation for Infectious Disease Care in Low- and Middle-Income Countries: Narrative Review

We took LMIC search terms from the Cochrane Effective Practice and Organisation of Care LMIC filters, defined according to the World Bank Classification (2022) [24]. We did not restrict the type of participants in the intervention (ie, we included health workers, patients, carers, general community members, and multiple types of participants). Intervention: We defined m Health interventions as per the WHO [6].

Josephine Greenall-Ota, H Manisha Yapa, Greg J Fox, Joel Negin

JMIR Mhealth Uhealth 2024;12:e55189

Cybersecurity Interventions in Health Care Organizations in Low- and Middle-Income Countries: Scoping Review

Cybersecurity Interventions in Health Care Organizations in Low- and Middle-Income Countries: Scoping Review

We included any health care–focused study undertaken in (1) an LMIC country that (2) described 1 or multiple interventions related to cyberattacks or cybersecurity (Textbox 1). Only studies published in English were included. Feasibility studies, commentaries, and editorial papers were excluded.

Kaede Hasegawa, Niki O'Brien, Mabel Prendergast, Chris Agape Ajah, Ana Luisa Neves, Saira Ghafur

J Med Internet Res 2024;26:e47311

Quality and Accountability of ChatGPT in Health Care in Low- and Middle-Income Countries: Simulated Patient Study

Quality and Accountability of ChatGPT in Health Care in Low- and Middle-Income Countries: Simulated Patient Study

We asked Chat GPT to act as a doctor in an LMIC and offer consultations. The SPs detailed their primary concerns, gave standardized responses to every question, and recorded all diagnoses and medication recommendations, which were cross-referenced with clinical guidelines to assess their accuracy and appropriateness. For a robust analysis, we presented each disease to Chat GPT 3 times. We conducted descriptive analyses with the final sample of 27 independent trials.

Yafei Si, Yuyi Yang, Xi Wang, Jiaqi Zu, Xi Chen, Xiaojing Fan, Ruopeng An, Sen Gong

J Med Internet Res 2024;26:e56121

Health Care Workers’ Motivations for Enrolling in Massive Open Online Courses During a Public Health Emergency: Descriptive Analysis

Health Care Workers’ Motivations for Enrolling in Massive Open Online Courses During a Public Health Emergency: Descriptive Analysis

This table shows differences by World Bank income classifications: high-income country (HIC), upper-middle–income country (UMIC), lower-middle–income country (LMIC), and low-income country (LIC). Mean ranking does not include observations that skipped ranking altogether (n=745). Course perspectives include observations that skipped ranking but provided responses for these questions. Generally, the fact that MOOCs were free was a lower-ranked motivator.

Jennifer Jones, Jamie Sewan Johnston, Ngouille Yabsa Ndiaye, Anna Tokar, Saumya Singla, Nadine Ann Skinner, Matthew Strehlow, Heini Utunen

JMIR Med Educ 2024;10:e51915