Luis Gerardo Vargas

School violence is a multidimensional public health crisis affecting children, families, schools, and communities. Yet conventional evaluations of school safety programs rely heavily on incident counts, suspensions, and law enforcement contacts, overlooking community perceptions, prevented incidents, and differences in local priorities. This editorial proposes a community-centered evaluation framework using the Analytic Hierarchy Process to integrate stakeholder judgments concerning the probability, severity, and manageability of school safety hazards. Pairwise comparisons and mathematically grounded aggregation procedures produce community-defined risk profiles against which prevention programs can be assessed. The framework aligns with public health approaches emphasizing surveillance, risk and protective factors, participatory intervention design, and evaluation across social-ecological levels. Its proposed implementation includes developing locally tailored models through school district site visits, deploying them through a web-based platform, and collecting longitudinal data to compare perceived program effectiveness across communities. By translating community engagement into measurable priorities, effectiveness scores, and cost-benefit assessments, the framework could support more defensible resource allocation, more responsive prevention strategies, and greater inclusion of communities disproportionately affected by violence.

Targeted medical decision‐making is a current strategy for addressing the heterogeneity in the patient population, especially when patients' preferences are included in the decision‐making process. In this paper, we propose a user‐customizable hybrid framework that can be adjusted at the patient group level to target a medical decision process. Our framework provides a flexible design, capable of balancing the gain from the reduction of provider time against the cost of prediction inaccuracy resulting from group customization. The framework combines a descriptive process, used to group the patients based on preference‐based subjective features, with a predictive process, which uses objective features to match a new patient with a group. We illustrate our approach by applying it to the colorectal cancer‐screening problem. The provider chooses what level of trade‐off is appropriate, as a function of the acceptable error level. The group customization process allows decision makers to better allocate scarce resources, by potentially shortening the time‐consuming process of modelling patients' preferences using individualised stability analysis. The proposed framework might be applied, with minor changes, to various medical decisions, or even to broader provider‐user scenarios, in which targeted decision‐making that includes user preferences is advantageous.

This paper presents a decision support system (DSS) for sentiment analysis of Spanish texts based on lexicons. The information provided by this DSS, named Spanish Sentiment Analysis‐DSS (SSA‐DSS), is employed to assess the social impacts considered in an external software module (RRPS‐PAT) centered on risk reduction of pandemic spread through passenger air transport. RRPS‐PAT is a complex multiobjective optimization module simultaneously addressing different conflicting objectives, including epidemiological, economic, and social aspects. This allows more effective and realistic decisions to be made. The specificity and novelty of the problem suggest the use of lexicon‐based approaches because there is no prior information about the problem to train machine learning–based approaches. The SSA‐DSS covers the entire process from the incorporation of texts, particularly tweets, to be analyzed, the application of preprocessing and cleaning tools, the selection of lexicons (general, context, and emoji lexicons) to be used and their possible modification, to the visualization of results and their exportation to other software tools. This paper contemplates, apart from the RRPS‐PAT module, the connection with a social network analysis tool (Gephi) that complements the information provided by SSA‐DSS with the identification of social leaders. The usefulness and functionalities of SSA‐DSS are illustrated by means of an example related to the evolution of societal mood in Spain during the COVID‐19 pandemic.

In 2018, the New York City (NYC) Department of Health and Mental Hygiene piloted a novel jurisdictional risk assessment (JRA) for public health disasters using the analytic hierarchy process (2018 JRA‐AHP). This new approach offers a replicable and equitable model for consensus decision‐making that incorporates the complexities of disaster preparedness. Its main contribution to the field is to offer a practical manifestation of emergency management's Whole Community Approach. The 2018 JRA‐AHP applied this principle by expanding traditional definitions of ‘expertise’ to include lived experience of disaster and facilitating direct participation in the risk decision by a variety of individuals with diverse backgrounds, expertise and knowledge of public health disasters. This paper describes the theory, methods and results behind the JRA‐AHP. The paper also presents a critical analysis of public health disaster risk assessments; contextualizes the Whole Community Approach using models of the relationship between democratic governments and communities; and addresses the practical applications of the 2018 JRA‐AHP in NYC while exploring the potential challenges that other jurisdictions may face in attempted adaptation.

Despite calls for community-oriented policing and the recognition that it results in improved relationships between the police and community as well as improved public security outcomes, police-community relations are arguably at an all-time low. Part of the challenge in achieving enhanced police-community relations is a disparate understanding of what each party wants and what each party can provide as well as prioritizing these factors. We present a project that worked to improve police-community relations through a conflict resolution process using the Analytic Hierarchy Process. The project was conducted with a group of police officers serving an urban, predominately African American community and representatives of the community over a period of several workshops. The workshops identified the goals, criteria, and objectives of each party as well as the perception of the other party’s goals, criteria, and objectives. The results of the meetings, the priorities generated, and similarities/divergences between them are presented.

In this paper, we apply the Analytic Hierarchy Process approach to conflict resolution in the context of the Russia–Ukraine conflict. We build models that illustrate the evaluation criteria, strategic and sub-criteria, and concessions for each party in this negotiation. Ratings are used to evaluate the degree to which concessions contribute or take away from successful resolution of the conflict. Afterwards, gain ratios are built to determine the benefit-cost scores so that concessions may be traded that result in equitable solutions. The approach presented here demonstrates for the first time why all concessions that parties to a conflict may offer might not trade all at once. A Max–Min optimization approach is used to maximize the gain to both parties of the conflict while minimizing the disparity in gain between the two.

In this paper, we analyse a proximal method based on the idea of forward-backward splitting for sampling from distributions with densities that are not necessarily smooth. In particular, we study the non-asymptotic properties of the Euler-Maruyama discretization of the Langevin equation, where the forward-backward envelope is used to deal with the non-smooth part of the dynamics. An advantage of this envelope, when compared to widely-used Moreu-Yoshida one and the MYULA algorithm, is that it maintains the MAP estimator of the original non-smooth distribution. We also study a number of numerical experiments that corroborate that support our theoretical findings.