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Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach

Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach

Figure 5 shows one Aboriginal woman who is low risk, labeled “Instance I,” it shows the specific contributions of each feature and their interactions in combination toward risk prediction for this individual by EBM. EBM accurately predicts this individual as “low risk” (class=0), with a high probability score of 0.904. The y-axes of the plot show impactful features and their corresponding values in brackets.

Guanjin Wang, Hachem Bennamoun, Wai Hang Kwok, Jenny Paola Ortega Quimbayo, Bridgette Kelly, Trish Ratajczak, Rhonda Marriott, Roz Walker, Jayne Kotz

J Med Internet Res 2025;27:e68030

Mobile App–Delivered Motivational Interviewing for Women on Eating Disorder Treatment Waitlists (MI-Coach: ED): Protocol for an App Development and Pilot Evaluation

Mobile App–Delivered Motivational Interviewing for Women on Eating Disorder Treatment Waitlists (MI-Coach: ED): Protocol for an App Development and Pilot Evaluation

Eligibility criteria were (1) being on a waitlist to receive services for an ED-specific concern and not for a separate mental disorder in British Columbia, (2) being at least 18 years of age, (3) self-identifying as a woman, (4) fluency in written and spoken English, (5) meeting the criteria for a diagnosis of a threshold or subthreshold ED (ie, BN, AN-R, AN-BP, BED, and OSFED), (6) absence of psychosis or diagnosis of a schizophrenia-spectrum disorder, and (7) absence of cognitive impairments or sensory deficits

Amané Halicki-Asakawa, Julia Mocci, Maya Libben

JMIR Res Protoc 2025;14:e66298

Net Reproduction Number as a Real-Time Metric of Population Reproducibility

Net Reproduction Number as a Real-Time Metric of Population Reproducibility

Therefore, the net reproduction rate (Rt), the number of daughters a woman of childbearing age would produce under prevailing fertility and mortality conditions, is better. Like other real-time epidemiological metrics (eg, the effective reproduction number in infectious disease modeling) [5], the Rt can be calculated and updated regularly with new population data; it can provide timely insights into population sustainability.

Chiara Achangwa, Changhee Han, Jun-Sik Lim, Seonghui Cho, Sangbum Choi, Sukhyun Ryu

JMIR Public Health Surveill 2025;11:e63603

Use of ChatGPT to Explore Gender and Geographic Disparities in Scientific Peer Review

Use of ChatGPT to Explore Gender and Geographic Disparities in Scientific Peer Review

The study also showed gender inequalities, with woman authors receiving less polite reviews than men. The study, limited to papers from a single journal (Nature Communications), aligns with the broader issue of gender discrimination in academic medicine [12-17]. While sentiment and politeness metrics may not directly measure bias, they serve as useful proxies for identifying potential biases in peer review.

Paul Sebo

J Med Internet Res 2024;26:e57667

Usability and Acceptability of a Conversational Agent Health Education App (Nthabi) for Young Women in Lesotho: Quantitative Study

Usability and Acceptability of a Conversational Agent Health Education App (Nthabi) for Young Women in Lesotho: Quantitative Study

The woman engages with the app by selecting a response from a multiple-choice menu that is updated at each turn of dialogue. To increase the accessibility and use of the system, a decision was made that the app would be fully downloadable to the user’s mobile phone, thereby enabling full content availability beyond the Wi-Fi environment. Use and information about the content discussed would be downloaded when the user was in a Wi-Fi environment.

Elizabeth Nkabane-Nkholongo, Mathildah Mpata-Mokgatle, Brian W Jack, Clevanne Julce, Timothy Bickmore

JMIR Hum Factors 2024;11:e52048