Enhancing mental health literacy in obsessive-compulsive disorder and reducing stigma via smartphone: A randomized controlled trial protocol.

Background: Obsessive-compulsive disorder (OCD) is a disabling disorder that can be successfully treated. However, individuals with OCD do not seek or delay seeking treatment. This delay may be explained by poor mental health literacy and stigmatizing attitudes toward OCD in community. In order to work on these variables, a gamified mental health mobile application (app) called esTOCma has been developed. The purpose of this study is to describe the protocol for a study to test the efficacy of esTOCma, increasing mental health literacy and help-seeking intention, reducing the stigmatizing attitudes and social distance suffered by people with OCD, as well as the distress associated with obsessive-compulsive symptoms. Methods: A randomized controlled trial with a crossover design with two conditions (immediate-use App group versus delayed-use App group) will be conducted on a non-clinical adult sample of the community of a minimum size of 200 participants. Participants in the immediate-use App group will start using the app at baseline until completion (10 days); whereas participants in the delayed-use App group will wait 10 days, and then start using the app until completion (10 days). The outcomes will be measured at four assessment points (baseline; 10 days from baseline; and 20 days from baseline; and after 3 months). The following instruments will be administered: Attribution Questionnaire, General Help-Seeking Questionnaire, Social Distance Scale, Mental Health Literacy, Psychoeducation Questionnaire, Social Desirability Scale, Single-Item Self-esteem Scale, and Obsessive-Compulsive Inventory-Revised. Discussion: This protocol presents the first study to describe a randomized control trial of a mental health app focused on changing mental health literacy, stigmatizing attitudes, social distance and help-seeking intention associated with OCD. An app intervention of these characteristics is especially relevant nowadays as the COVID-19 pandemic has increased obsessive-compulsive symptoms and severity. An improvement in general knowledge about OCD and a reduction in stigma could be associated with earlier OCD detection and an increase in help-seeking intention, which could result in greater wellbeing. Moreover, normalizing intrusions and knowledge about the cognitive OCD model could serve as a protective variable in vulnerable individuals. Trial registration: ClinicalTrials.gov identifier: NCT04777292. Registered February 23, 2021, https://clinicaltrials.gov/ct2/show/NCT04777292.

The dial-a-ride problem with private fleet and common carrier

Dial-a-ride problems aim to design the least-costly door-to-door vehicle routes for transporting individual users, subject to several service constraints like time windows, service and route durations, and ride-time. In some cases, providers cannot meet the demand and may outsource some requests. In this paper, we introduce, model, and solve the dial-a-ride problem with private fleet and common carrier (DARP-PFCC) that makes it possible to transfer the demand unmet by the provider to mobility-on-demand services and taxis. All outsourced vehicles are assumed to be available at any instant of the day and have unlimited capacity, enabling to satisfy all user requests, particularly during peak times. We implement a branch-and-cut (B&C) algorithm based on an exact method from the literature to solve the DARP-PFCC, and we develop a near parameter-free parallel metaheuristic to handle large instances. Our metaheuristic combines the Biased Random-key Genetic Algorithm (BRKGA) and the Q-learning (QL) method into the same framework (BRKGA-QL), in which an agent helps to use feedback information to dynamically choose the parameters of BRKGA during the search to select the most appropriate configuration to solve a specific problem instance. Both algorithms are flexible enough to solve the classical DARP, and extensive computational experiments demonstrate the efficiency of our methods. For the DARP instances, the B&C proved optimality for 41 of the 42 instances tested in a reasonable computational time, and the BRKGA-QL found the best-known solution for these instances within a matter of seconds. These results indicate that our metaheuristic performs equally well than state-of-the-art DARP algorithms. In the DARP-PFCC experiments on a set of 504 small-size instances, B&C proved optimality for 497 instances, while BRKGA-QL found 452 optimal solutions, totalling 90.94% of the instances solved to optimality. Finally, we present the results for a real case study for the DARP-PFCC, where BRKGA-QL solved very large problem instances containing up to 713 transportation requests. We also derive some managerial analyses to assess the effects of vehicle capacity reduction, for example due to the COVID-19 pandemic, on shared transportation. The results point to the benefits of combining the private fleet and common carriers in dial-a-ride problems, both for the provider and for the users.

Forecast UTI: aplicación para prever camas en Unidades de Cuidados Intensivos en el contexto de la pandemia de COVID-19 Forecast UTI: application for predicting intensive care unit beds in the context of the COVID-19 pandemic

Resumo Frente à necessidade de gerenciamento e previsão do número de leitos de unidades de terapia intensiva (UTIs) para pacientes graves de COVID-19, foi desenvolvido o Forecast UTI, um aplicativo de livre acesso, que permite o monitoramento de indicadores hospitalares com base em dados históricos do serviço de saúde e na dinâmica temporal da epidemia por coronavírus. O Forecast UTI também possibilita realizar previsões de curto prazo do número de leitos ocupados pela doença diariamente, e estabelecer possíveis cenários de atendimento. Este artigo apresenta as funções, modo de acesso e exemplos de uso do Forecast UTI, uma ferramenta computacional destinada a auxiliar gestores de hospitais da rede pública e privada do Sistema Único de Saúde (SUS) no subsídio à tomada de decisão, de forma rápida, estratégica e eficiente. Resumen En vista de la necesidad de administrar y prever el número de camas en la Unidad de Cuidados Intensivos para pacientes graves de COVID-19, se desarrolló Forecast UTI: una aplicación de acceso abierto que permite el monitoreo de indicadores hospitalarios basados en datos históricos del servicio salud y la dinámica temporal de esta epidemia por coronavirus También es posible hacer pronósticos a corto plazo del número de camas ocupadas diariamente por la enfermedad y establecer posibles escenarios de atención. Este artículo presenta las funciones, el modo de acceso y ejemplos de uso de Forecast UTI, una herramienta computacional capaz de ayudar a los gestores de hospitales públicos y privados en el Sistema Único de Salud, ya que apoyan la toma de decisiones de manera rápida, estratégica y eficiente. In view of the need to manage and forecast the number of Intensive Care Unit (ICU) beds for critically ill COVID-19 patients, the Forecast UTI open access application was developed to enable hospital indicator monitoring based on past health data and the temporal dynamics of the Coronavirus epidemic. Forecast UTI also enables short-term forecasts of the number of beds occupied daily by COVID-19 patients and possible care scenarios to be established. This article presents the functions, mode of access and examples of uses of Forecast UTI, a computational tool intended to assist managers of public and private hospitals within the Brazilian National Health System by supporting quick, strategic and efficient decision-making.

Forecast UTI: application for predicting intensive care unit beds in the context of the COVID-19 pandemic. / Forecast UTI: aplicativo para previsão de leitos de unidades de terapia intensiva no contexto da pandemia de COVID-19.

In view of the need to manage and forecast the number of Intensive Care Unit (ICU) beds for critically ill COVID-19 patients, the Forecast UTI open access application was developed to enable hospital indicator monitoring based on past health data and the temporal dynamics of the Coronavirus epidemic. Forecast UTI also enables short-term forecasts of the number of beds occupied daily by COVID-19 patients and possible care scenarios to be established. This article presents the functions, mode of access and examples of uses of Forecast UTI, a computational tool intended to assist managers of public and private hospitals within the Brazilian National Health System by supporting quick, strategic and efficient decision-making.

Frente à necessidade de gerenciamento e previsão do número de leitos de unidades de terapia intensiva (UTIs) para pacientes graves de COVID-19, foi desenvolvido o Forecast UTI, um aplicativo de livre acesso, que permite o monitoramento de indicadores hospitalares com base em dados históricos do serviço de saúde e na dinâmica temporal da epidemia por coronavírus. O Forecast UTI também possibilita realizar previsões de curto prazo do número de leitos ocupados pela doença diariamente, e estabelecer possíveis cenários de atendimento. Este artigo apresenta as funções, modo de acesso e exemplos de uso do Forecast UTI, uma ferramenta computacional destinada a auxiliar gestores de hospitais da rede pública e privada do Sistema Único de Saúde (SUS) no subsídio à tomada de decisão, de forma rápida, estratégica e eficiente.

En vista de la necesidad de administrar y prever el número de camas en la Unidad de Cuidados Intensivos para pacientes graves de COVID-19, se desarrolló Forecast UTI: una aplicación de acceso abierto que permite el monitoreo de indicadores hospitalarios basados en datos históricos del servicio salud y la dinámica temporal de esta epidemia por coronavirus También es posible hacer pronósticos a corto plazo del número de camas ocupadas diariamente por la enfermedad y establecer posibles escenarios de atención. Este artículo presenta las funciones, el modo de acceso y ejemplos de uso de Forecast UTI, una herramienta computacional capaz de ayudar a los gestores de hospitales públicos y privados en el Sistema Único de Salud, ya que apoyan la toma de decisiones de manera rápida, estratégica y eficiente.

A mathematical model for the coverage location problem with overlap control.

The Coverage Location Problem (CLP) seeks the best locations for service to minimize the total number of facilities required to meet all demands. This paper studies a new variation of this problem, called the Coverage Location Problem with Overlap Control (CLPOC). This problem models real contexts related to overloaded attendance systems, which require coverage zones with overlaps. Thus, each demand must be covered by a certain number of additional facilities to ensure that demands will be met even when the designated facility is unable to due to some facility issue. This feature is important in public and emergency services. We observe that this number of additional facilities is excessive in some demand points because overlaps among coverage zones occur naturally in CLP. The goal of the CLPOC is to control overlaps to prioritize regions with a high density population or to minimize the number of coverage zones for each demand point. In this paper, we propose a new mathematical model for the CLPOC that controls the overlap between coverage zones. We used a commercial solver to find the optimal solutions for available instances in the literature. The computational tests show that the proposed mathematical model found appropriate solutions in terms of number of demand points with minimum coverage zones and sufficient coverage zones for high demand points.