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Online Safety When Considering Self-Harm and Suicide-Related Content: Qualitative Focus Group Study With Young People, Policy Makers, and Social Media Industry Professionals

Online Safety When Considering Self-Harm and Suicide-Related Content: Qualitative Focus Group Study With Young People, Policy Makers, and Social Media Industry Professionals

To this end, it appears that national suicide prevention strategies and online safety policies are yet to appropriately consider how self-harm and suicide are expressed, and managed, on social media, leaving a key gap in youth online safety policy and practice. The objective of this study was to inform health, communication, and online safety policy as it relates to self-harm and suicide prevention.

Louise La Sala, Amanda Sabo, Maria Michail, Pinar Thorn, Michelle Lamblin, Vivienne Browne, Jo Robinson

J Med Internet Res 2025;27:e66321

Generative Artificial Intelligence in Medical Education—Policies and Training at US Osteopathic Medical Schools: Descriptive Cross-Sectional Survey

Generative Artificial Intelligence in Medical Education—Policies and Training at US Osteopathic Medical Schools: Descriptive Cross-Sectional Survey

It is unclear whether COMs have included training or policy guidance in this domain. This study aimed to describe the status of policy and training, specifically in one aspect of AI, Gen AI, for medical students, faculty, and administrators, as well as near-term plans for policy and training development at COMs. This analysis will provide an overview of the current state of Gen AI integration in osteopathic medical education, which will demonstrate opportunities for future development.

Tsunagu Ichikawa, Elizabeth Olsen, Arathi Vinod, Noah Glenn, Karim Hanna, Gregg C Lund, Stacey Pierce-Talsma

JMIR Med Educ 2025;11:e58766

Regulation and Compliance in Telemedicine: Viewpoint

Regulation and Compliance in Telemedicine: Viewpoint

Lawmakers made these changes to increase access to health care nationwide during the pandemic and based these flexibilities on previously considered legislative, regulatory, and policy updates [5]. The expiration of the PHE in May 2023 created a dilemma for lawmakers, who were unprepared to make decisions about long-term telemedicine policy. To avoid negative impacts and allow more time for decision-making, they extended many telemedicine-related policy flexibilities through December 31, 2024 [6,7].

Julia Ivanova, Mollie R Cummins, Triton Ong, Hiral Soni, Janelle Barrera, Hattie Wilczewski, Brandon Welch, Brian Bunnell

J Med Internet Res 2025;27:e53558

Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study

Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study

However, using only the previous number of cases cannot reflect temporal variation due to external factors such as outbreaks with social events, new variants, and quarantine policy. To mitigate this limitation, some studies used additional features including traffic [9,10], mobile roaming [11], and search engine query [12] data. These approaches have demonstrated improvements in forecasting performance by reflecting human behavior and interest during the COVID-19 pandemic.

Seong-Ho Ahn, Kwangil Yim, Hyun-Sik Won, Kang-Min Kim, Dong-Hwa Jeong

J Med Internet Res 2024;26:e63476

The University of California Study of Outcomes in Mothers and Infants (a Population-Based Research Resource): Retrospective Cohort Study

The University of California Study of Outcomes in Mothers and Infants (a Population-Based Research Resource): Retrospective Cohort Study

The SOMI data platform allows multidisciplinary researchers, policy makers, and other stakeholders to visualize and quantify maternal and child health outcomes and related disparities across San Diego County and California and to examine trends over time. In this paper, we provide an overview of SOMI. The mission of SOMI is to predict and promote healthy outcomes in birthing people, infants, and children in collaboration with community and public health partners.

Rebecca J Baer, Gretchen Bandoli, Laura Jelliffe-Pawlowski, Christina D Chambers

JMIR Public Health Surveill 2024;10:e59844

Using Artificial Intelligence to Detect Risk of Family Violence: Protocol for a Systematic Review and Meta-Analysis

Using Artificial Intelligence to Detect Risk of Family Violence: Protocol for a Systematic Review and Meta-Analysis

Definitions of family violence vary in the research literature, which has implications for reported rates of family violence and, importantly, policy [1]. For this article, family violence will be defined by Section 4 AB (1) of the Australian Family Law Act 1975 as “threatening or other behavior by a person that coerces or controls a member of the person’s family (the family member) or causes the family member to be fearful.”

Kathleen de Boer, Jessica L Mackelprang, Maja Nedeljkovic, Denny Meyer, Ravi Iyer

JMIR Res Protoc 2024;13:e54966

Spatial Analyses of Crisis Pregnancy Centers and Abortion Facilities in the United States, 2021 (Pre-Dobbs): Cross-Sectional Study

Spatial Analyses of Crisis Pregnancy Centers and Abortion Facilities in the United States, 2021 (Pre-Dobbs): Cross-Sectional Study

Since all CPCs and abortion facilities are deidentified and the research involved no interaction with or information about human participants, this study was not submitted for Institutional Review Board approval per the policy of the Human Research Protection Program (subsections 2.2 and 3.6) of the University of Georgia [45]. First, we calculated the ratio of abortion facilities nationally and by region, division, and state and mapped the geolocations of CPCs and abortion facilities [46].

Andrea Swartzendruber, Nicole Luisi, Erin R Johnson, Danielle N Lambert

JMIR Public Health Surveill 2024;10:e60001

Exploring Physicians’ Perceptions of Digital Health’s Impact on the Patient-Physician Relationship in the Primary Health Care Setting: Qualitative Descriptive Study

Exploring Physicians’ Perceptions of Digital Health’s Impact on the Patient-Physician Relationship in the Primary Health Care Setting: Qualitative Descriptive Study

Jacob et al [25] described the adoption and use factors as social and personal factors, technical and material factors, and organization and policy factors [25]. By combining these 2 frameworks (Figure 1), we were able to cover issues related to physician,” “patient,” “digital health,” and “trust,” as well as interdomain issues such as “patient-physician relationship,” “positive and negative experiences,” and “technology compatibility.” Combined conceptual framework.

Kai Ping Sze, Qi Wei Fong, Jacqueline Giovanna De Roza, Eng Sing Lee, Shu Yun Tan

J Med Internet Res 2024;26:e53705