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.