Settings with insufficient human resources struggle to provide timely eye care services and information to the population. mHealth (mobile healthcare) is a promising solution; however, evidence on the effectiveness of interactive voice response (IVR) and real-time phone-based education remains scarce, despite their potential to be scalable and cost-effective. This study aims to implement the Virtual Baithak, an interactive mHealth platform, to improve eye-health literacy among older adults residing in rural India. The objectives are to (1) Develop and validate the Virtual Baithak for improving vision health and (2) Determine its effectiveness, feasibility and acceptability among the older adults.

This 3-armed, parallel, randomised controlled trial of 14 months duration will enrol 381 older adults (aged 60 years and above). Participants will be blinded and randomly (computer-generated) assigned to either of the three groups based on the intervention for eye-health education they receive: both IVR and group calls moderated by a healthcare professional, only IVR and usual care. The two intervention arms will receive the information weekly over a 3-month period through the Virtual Baithak platform, which will be designed for this study using a participatory research approach to develop the content. The primary study outcomes are digital health literacy and vision health knowledge scores, measured at baseline and 14 months. The secondary outcomes include m-health technology acceptance and usage practices. A mixed-method process evaluation will be conducted to assess the intervention feasibility and implementation, including in-depth interviews with participants. The qualitative data will be thematically analysed to explore factors that promote or restrain the implementation. The inferential statistical quantitative analysis will be performed using linear mixed models.

The study has been approved by the ‘Institute Ethics Committee,’ PGIMER, Chandigarh, India (PGI/IEC/2022/EIC000282 dated 18 February 2022). The results will be disseminated via presentations and/or publications at the national and international levels.

CTRI/2023/02/049383, dated 1 February 2023.

Suicide rates have increased over the last couple of decades globally, particularly in the United States and among populations with lower economic status who present at safety-net healthcare systems. Recently, predictive models for suicide risk have shown promise; however, a model for this specific population does not exist.

To develop a predictive risk model of suicide and intentional self-harm (ISH) for patients presenting at the psychiatric emergency department (ED) of JPS Health Network, a safety net medical and mental healthcare system in Texas.

The study used structured and unstructured electronic medical record (EMR) data (2015–2019) and local medical examiner data (2015–2020) to create predictors and outcome variables. All psychiatric ED notes during calendar years 2018 and 2019 were reviewed using natural language processing to identify presentations for any level of self-harm and subsequent manual review of identified visits to accurately classify ED presentations for treatment of an act of intentional self-harm meeting study criteria. Data from 15 987 patients were used to develop and validate a machine learning-based predictive model that leverages rolling window methodology to predict risk repeatedly across a patient’s trajectory. Feature engineering played a prominent role in defining new predictors.

The best model (XGBoost) achieved the area under the receiver operating characteristic curve of 0.81 for 30-day predictions and demonstrated concentration of ISH and suicide attempt events in high-risk quantiles of risk (65% had events in top 0.1% quantile). The predicted risk can be translated into a propensity of events (80% at the highest predicted risk) to facilitate clinical interpretation.

Machine learning-based models can be used with standard EMRs to identify patients presenting at the psychiatric ED with a high risk of ISH and suicide attempts within the next 30 days.

Integration of a predictive model can significantly aid clinical decision-making in safety-net psychiatric EDs.

Community health workers (CHWs) are trained lay people and trusted members of communities worldwide who play crucial roles in bridging healthcare gaps in low–middle-income countries yet remain underused and not well integrated within high-income countries like Canada. The objective of this scoping review is to map out available evidence on the integration of CHWs in high-income countries with universal healthcare systems.

This scoping review will include all available literature involving CHWs, or similar designations, and their integration into universal health systems within high-income countries. Literature will be excluded if it does not involve CHWs, universal healthcare systems, address integration or is conducted in low–middle-income countries. This review will include all available literature (including those that show null or negative results) that examines the integration of CHWs in high-income countries with a universal healthcare system. Documents describing integration may include, but are not limited to: tools, policies, models, frameworks, programmes or organisational features that seek to promote positive integration. Peer-reviewed and grey literature examining CHW integration in high-income countries with universal healthcare systems will be eligible for inclusion. Databases/sources to be searched (from inception until November 2025) will include: Medline (Ovid), Embase (Elsevier), Scopus (Elsevier), CINAHL (EBSCO), PsycINFO (EBSCO), Academic Search Premier (EBSCO), Business Source Complete (EBSCO), ProQuest Dissertations and Theses Global. Retrieval of full-text, all language studies (and other literature), data extraction, synthesis and mapping will be performed independently by two reviewers, following Joanna Briggs Institute methodology. Findings will be organised and presented according to the Levesque conceptual framework for healthcare access.

Ethics approval is not required for this scoping review and literature search will start in October 2025 or on acceptance of this protocol. The findings of the scoping review will be available (February 2026) and will be published in a peer-reviewed journal.