Logic Model Research Articles

Expanding Outcomes in Cancer Screening Safety Net Programs: Promoting Sustainability and Policy Reform.

Community-engaged patient navigation safety net programs are established as an evidence-based approach to address cancer prevention and early detection efforts, but barriers to expand and sustain such programs persist. In addition, few studies describe how these programs impact buy-in among communities and policy change within health care systems and government. We describe how we used the Capacity for Sustainability Framework to guide efforts for program sustainability and community, institutional, and policy level change in a breast cancer screening and patient navigation safety net program. The nine domains of the Capacity for Sustainability Framework were used to develop program logic models, to inform program implementation and quality improvement agendas, and to guide multi-level partner and stakeholder engagement, outreach, and dissemination of outcomes. The program is currently in its seventh year and continues to be annually funded by a city public health department. In 2021, additional 5-year renewable funding from a state public health department was secured. In addition, institutional program support was expanded for patients diagnosed with breast cancer. Program leaders worked with policymakers to draft legislation to support training certification and third-payor reimbursement for patient navigators and community health workers. The program is well-known and trusted among community members, community-based organizations, and providers. Community, organizational, and policy-level outcomes demonstrate that community-engaged patient navigation safety net programs can influence more than individual and interpersonal outcomes and can be sustained over time.

Adapting a complex violence prevention intervention: a case study of the Good School Toolkit in Uganda

BackgroundAdaptation is a key strategy to extend the reach of evidence-based interventions to prevent violence in new populations, but there is a dearth of practical case examples. The Good School Toolkit was developed by Ugandan NGO Raising Voices for use in primary schools (GST-P). We describe our systematic approach to adapting the GST-P for use in secondary schools in Uganda, and reflect on the utility of the process as well as limitations of existing adaptation frameworks.MethodsWe adapted the GST-P in four phases, which included: I) clarifying the logic model and core intervention components using a streamlined process; II) conducting formative research (cross-sectional survey, focus groups, etc.) to understand the new population; III) selecting and preparing new intervention components and modifying existing intervention components; and IV) pretesting new intervention components with teachers and students in Uganda.ResultsWe identified core components using a logic model. Formative research showed results largely in line with our apriori hypotheses. Teacher violence remained highly prevalent in secondary versus primary schools (> 65% of secondary students reported past year exposure), while peer violence significantly increased (secondary = 52% vs. primary girls = 40%, P < 0.001; secondary = 54% vs. primary boys = 44%, P = 0.009) in secondary versus primary schools. Significantly more secondary girls (51%) than secondary boys (45%) reported past year dating/intimate partner violence (P = 0.03). Inequitable, gendered educational practices emerged as a salient theme, perceived to heighten female students’ vulnerability to violence. In light of these findings, we made several adjustments to the adapted intervention. We strengthened existing teacher and peer violence intervention components. We also developed, pretested and revised new program components to prevent dating violence and promote ‘gender fairness in schools’. Finally, original activities were modified to support engagement with school administration and promote increased student agency in secondary schools.ConclusionsBased on our experience, it was difficult to apply mechanistic models to clarify the intervention logic of the GST-P, a complex multicomponent intervention, and simpler methods may be sufficient. Our team had high levels of contextual knowledge before the adaptation, and formative research to understand the new target population provided only limited additional insight. In similar situations, a simplified approach to mapping the core intervention components, qualitative research to understand the new target population, and pre-testing of new intervention components may be the most informative elements of systematic adaptation processes.

Adjunctive interventions: change methods directed at recipients that support uptake and use of health innovations

BackgroundImplementation science groups change methods into two categories: (1) clinical, behavioral, or biomedical intervention targeting recipient’s health outcomes and (2) implementation strategies targeting the delivery system. Differentiating interventions from strategies based on their intended functions is critical to accurately attributing their effects to health or implementation outcomes. However, in coordinating 200+ HIV implementation research projects and conducting systematic reviews, we identified change methods that had characteristics of both interventions and strategies that were inconsistently categorized. To alleviate confusion and improve change method specification, we propose that implementation science should adopt an extant but rarely used term—adjunctive interventions—to classify change methods that are distinct from the common intervention/strategy taxonomy.Main textAdjunctive interventions as change methods that target recipients (e.g., patients, participants) of a health intervention but are designed to increase recipients’ motivation, self-efficacy, or capacity for initiating, adhering to, complying with, or engaging with the health intervention over time. In two of our published reviews on implementation of HIV interventions, 25 out of 45 coded change methods fell into this gray area between strategy and intervention. We also noted instances in which the same change method was labelled as the intervention (“the thing”), as an adjunctive intervention, or an implementation strategy in different studies—further muddying the waters. Adjunctive interventions are distinguished from other change methods by their intended targets, desired outcomes, and theory of action and causal processes. Whereas health interventions target recipients and have a direct, causal effect on the health outcome, adjunctive interventions enhance recipients’ attitudes and behaviors to engage with the intervention and have an indirect causal link to the health outcome via increasing the probability of recipients’ utilization and adherence to the intervention. Adjunctive interventions are incapable of directly producing the health outcome and will themselves require implementation strategies to effectively impact sustained uptake, utilization, and adherence. Case examples, logic modeling, and considerations (e.g., relationship to consumer engagement strategies) for adjunctive intervention research are provided.ConclusionConceptualizing adjunctive interventions as a separate type of change method will advance implementation research by improving tests of effectiveness, and the specification of mechanisms and outcomes.

MetaLo: metabolic analysis of Logical models extracted from molecular interaction maps.

Molecular interaction maps (MIMs) are static graphical representations depicting complex biochemical networks that can be formalized using one of the Systems Biology Graphical Notation languages. Regardless of their extensive coverage of various biological processes, they are limited in terms of dynamic insights. However, MIMs can serve as templates for developing dynamic computational models. We present MetaLo, an open-source Python package that enables the coupling of Boolean models inferred from process description MIMs with generic core metabolic networks. MetaLo provides a framework to study the impact of signaling cascades, gene regulation processes, and metabolic flux distribution of central energy production pathways. MetaLo computes the Boolean model's asynchronous asymptotic behavior, through the identification of trap-spaces, and extracts metabolic constraints to contextualize the generic metabolic network. MetaLo is able to handle large-scale Boolean models and genome-scale metabolic models without requiring kinetic information or manual tuning. The framework behind MetaLo enables in depth analysis of the regulatory model, and may allow tackling a lack of omics data in poorly addressed biological fields to contextualize generic metabolic networks along with improper automatic reconstructions of cell- and/or disease-specific metabolic networks. MetaLo is available at https://pypi.org/project/metalo/ under the terms of the GNU General Public License v3.

Co-design of the EMBED-Care Framework as an intervention to enhance shared decision-making for people affected by dementia and practitioners, comprising holistic assessment, linked with clinical decision support tools: A qualitative study.

Shared decision-making intends to align care provision with individuals' preferences. However, the involvement of people living with dementia in decision-making about their care varies. We aimed to co-design the EMBED-Care Framework, to enhance shared decision-making between people affected by dementia and practitioners. A theory and evidence driven co-design study was conducted, using iterative workshops, informed by a theoretical model of shared decision-making and the EMBED-Care Framework (the intervention) for person-centred holistic palliative dementia care. The intervention incorporates a holistic outcome measure for assessment and review, linked with clinical decision-support tools to support shared decision-making. We drew on the Medical Research Council (MRC) guidance for developing and evaluating complex interventions. Participants included people with dementia of any type, current or bereaved family carers and practitioners. We recruited via established dementia groups and research and clinical networks. Data were analysed using reflexive thematic analysis to explore how and when the intervention could enhance communication and shared decision-making, and the requirements for use, presented as a logic model. Five co-design workshops were undertaken with participants comprising people affected by dementia (n = 18) and practitioners (n = 36). Three themes were generated, comprising: (1) 'knowing the person and personalisation of care', involving the person with dementia and/or family carer identifying the needs of the person using a holistic assessment. (2) 'engaging and considering the perspectives of all involved in decision-making' required listening to the person and the family to understand their priorities, and to manage multiple preferences. (3) 'Training and support activities' to use the Framework through use of animated videos to convey information, such as to understand the outcome measure used to assess symptoms. The intervention developed sought to enhance shared decision-making with individuals affected by dementia and practitioners, through increased shared knowledge of individual priorities and choices for care and treatment. The workshops generated understanding to manage disagreements in determining priorities. Practitioners require face-to-face training on the intervention, and on communication to manage sensitive conversations about symptoms, care and treatment with individuals and their family. The findings informed the construction of a logic model to illustrate how the intervention is intended to work.

Smart city: an advanced framework for analyzing public sentiment orientation toward recycled water

The coronavirus pandemic of the past several years has had a profound impact on all aspects of life, including resource utilization. One notable example is the increased demand for freshwater, a lifeblood of our planet, on the other hand, the smart city vision aims to attain a smart water management goal by investing in innovative solutions such as recycled water systems. However, the problem lies in the public’s sentiment and willingness to use this new resource which discourages investors and hinders the development of this field. Therefore, in our work, we applied sentiment analysis using an extended version of the fuzzy logic and neural network model from our previous work, to find out the general public opinion regarding recycled water and to assess the effects of sentiments on the public’s readiness to use this resource. Our analysis was based on a dataset of over 1 million text content from 2013 to 2022. The results show, from spatio-temporal perspectives, that sentiment orientation and acceptance-behavior towards using recycled water have increased positively. Additionally, the public is more concerned in areas driven by the smart city vision than in areas of medium and low economic development, where investment in sensibilization campaigns is needed.

“Whitewashing” the activities of real estate agencies as one of the directions for ensuring the economic security of public legal entities in the budgetary and tax sphere

The subject of the study is the relationships that arise in the process of ensuring fiscal security by public legal entities. Fiscal security is considered in the article from the perspective of its duality (budgetary security and tax security) in the context of the shadow sector, taking into account the risks of cartel collusion and concealment of part of the income and property from taxation. The nature of the formation of the shadow sector is explored, which is defined as a deep reserve that should be gradually mobilized into the budget system, while encouraging taxpayers to conscientiously fulfill their tax obligations, which will subsequently form a completely different level of tax culture. The article presents the author's logical model of leveling the shadow economy of public legal entities in order to ensure their fiscal security based on digital tools of public administration. The research is based on the following methods of scientific knowledge: comparison, analysis, synthesis of theoretical material. The author has developed an algorithm for comparing open Internet data with Federal Tax Service data in order to identify hidden income. The scientific novelty of the study lies in the development of the category “fiscal security”, the theoretical justification of the directions for its provision by public legal entities in the context of the “whitewashing” of the economy in the context of the use of digital tools of public administration.

Adaptive hybrid reasoning for agent-based digital twins of distributed multi-robot systems

The digital twin (DT) mainly acts as a virtual exemplification of a real-world entity, system, or process via multiphysical and logical models, allowing the capture and synchronization of its functions and attributes. The bridge between the actual system and the digital realm can be utilized to optimize the system’s performance, and forecast and predict its behavior. Incorporating intelligent and adaptive reasoning mechanisms into DTs is crucial to enable them to reason, adapt, and take efficacious actions in complex and dynamic environments. To this end, we introduce an approach for deploying agent-based DTs for cyber-physical systems. The foundation pillars of this approach are (1) integrating the concepts, entities, and relations of Zeigler’s modeling and simulation framework from the perspective of agent-based DTs; (2) utilizing an expandable and scalable architecture for designing and materializing these twins, which handily enables extending and scaling physical and digital assets of the system; and finally (3) a two-tier reasoning strategy; reactive and rational models are conceptually redefined in the context of the modeling and simulation framework of agent-based DTs and technically deployed to boost the adaptive reasoning and decision-making function of DTs. As a result, an implemented simulation and control platform for a multi-robot system demonstrates the approach’s applicability and feasibility, manifesting its usability and efficiency. The platform represents physical entities as autonomous agents, creates their DTs, and assigns adequate reasoning capability to promote adaptive planning, autonomous resource management, and flexible logical decision-making to handle different situations and scenarios.

Determination of Electricity Production by Fuzzy Logic Method

With the increase in the need for electrical energy, production amount planning is of great importance in order not to experience restrictions in terms of use, to meet the required electricity production, and to evaluate the excess production efficiently. In this study, a generation forecasting model was created with the fuzzy logic method to determine the electricity generation strategy. The created model is aimed to determine the electrical energy that needs to be produced daily by using the previous day's production amount, temperature, and season data. Three separate sets of data were used to test the fuzzy logic model built using information from the General Directorate of Meteorology (GDM) and Energy Markets Operations Inc. (EMOI). Fuzzy Logic was used to predict the data and the accuracy rates were found to be high. An improvement was observed when the accuracy rates were compared with the accuracy rates obtained in the Multiple Linear Regression Model. The accuracy rates of the model were initially examined using the Fuzzy Logic approach on weekdays and weekends, followed by a seasonal analysis and an assessment of the model's performance. As a result of the analysis, it was observed that the model worked with high accuracy in the autumn season and on weekend days.

Solving systems of homogeneous linear algebraic equations in pricing problems of an alternative financial model

Subject. This article focuses on the problems of solving systems of homogeneous linear algebraic equations in the pricing problems of an alternative financial model. Objectives. The article aims to develop a non-trivial approach to solving the problem of pricing from the scratch in the construction of a national alternative financial model, which boils down to solving large homogeneous systems of linear algebraic equations, complicated by the fuzzy setting of initial parameters (initial data) within the framework of creating a State model of a socially oriented economy, guaranteed to ensure economic, military and other types of integrated security of the Russian Federation. Methods. For the study, we used tensorial notations involving the use of elements of linear algebra, including operations with matrices and vectors, the apparatus of the fuzzy set theory (interval arithmetic), as well as methods and principles of scientific research and complex logical analysis and modeling of pricing and technological processes. Results. The article shows that an effective non-zero (different from zero, i.e. classical, trivial) solution of systems of homogeneous algebraic equations in pricing problems of an alternative financial model from the scratch is possible if the initial parameters are specified by fuzzy sets, which significantly simplifies the procedure of the computational process and the volume of arithmetic operations. Conclusions. The results obtained can be used in structures involved in the creation and testing of financial and economic models both at the federal and the Russian Federation constituent entity levels, as well as in governmental and non-governmental organizations dealing with the issues of pricing, planning and logistics support for business entities not only in the Russian Federation, but also in other countries experiencing problems in the field of financing.

Fuzzy Logic Model for Assessing Accident Proneness Based on Passenger Vehicle Speed in Real and Virtual Traffic Conditions

Fuzzy Logic Model for Assessing Accident Proneness Based on Passenger Vehicle Speed in Real and Virtual Traffic Conditions

Revising the Logic Model Behind Health Care's Social Care Investments.

Policy Points This article summarizes recent evidence on how increased awareness of patients' social conditions in the health care sector may influence health and health care utilization outcomes. Using this evidence, we propose a more expansive logic model to explain the impacts of social care programs and inform future social care program investments and evaluations.

Energy digital twins in smart manufacturing systems: A case study

Among core Industry 4.0 technologies, the Digital Twin represents a promising smart technology and tool that researchers are investigating to help improve energy efficiency at the shopfloor level by analyzing and optimizing energy consumption. Energy Digital Twins are a relatively new area of research, only gaining popularity in industry applications and academia recently. where a comprehensive literature review uncovered only one ‘true’ Energy Digital Twin application across published research to date. To address this research gap, an Energy Digital Twin for smart manufacturing systems was developed and evaluated in this study. In particular, the study focused on bidirectional parameter communication between the physical and the virtual part with the aim of optimizing the energy used in the manufacturing process. To address this research gap, the research objective is to create and evaluate an application of an energy optimizing Digital Twin for a Heating Tunnel. Following the definition of a Digital Twin, the research methodology and experimental setup have three major components: i) the Heating Tunnel as the physical object, ii) the digital counterpart constructed using Python to house the digital control logic and linear energy optimization feedback model, and iii) the connecting fabric, in form of a bidirectional OPC UA communication protocol. The optimization model ingests input parameters of setpoint temperature, power level, and targeted overshoot time, and after running the simulation, returns a calculated value of the required turn off temperature to the real-time heating process of the physical system. Results show that the Energy Digital Twin is effective at maintaining the maximum temperature range of the Heating Tunnel during the heating process, in addition to reducing the energy consumption and cost for all trial runs compared to the original process. Overall, the study successfully created and evaluated a functioning energy optimizing Digital Twin with bidirectional, automated feedback. The results of this research emphasize the potential impact of Energy Digital Twin applications in any manufacturing process and show the promise of future work in this realm of Energy Digital Twins.

The Role of HL7 FHIR in the European Project GATEKEEPER.

The European Project GATEKEEPER aims to develop a platform and marketplace to ensure a healthier independent life for the aging population. In this platform the role of HL7 FHIR is to provide a shared logical data model to collect data in heterogeneous living, which can be used by AI Service and the Gatekeeper HL7 FHIR Implementation Guide was created for this purpose. Independent pilots used this IG and illustrate the impact of the approach, benefit, value, and scalability.

Digital Health Safety Matters: A Promising Practice Study into the Adoption of Patient Safety Guidelines in Australia.

A foundation for digitally enabling healthier living is the safe development and use of technology. The practice of digital health safety has emerged from patient harm attributed to failing technologies. The study aimed to investigate how to adopt and implement digital health safety guidelines at scale. Data was collected through an online survey, semi-structured interviews, focus groups, document review, and data mining of artefacts. The findings of this study capture the emerging practice from Australia in a way that offers insights into the problem of practice, patient safety practice, safety culture, and socio-technical factors. The research findings contribute to better understanding of the complexities of balancing digital innovation with patient safety. The four recommendations from the study and the provision of a logic model will support the audience to implement actions toward a safer digital health ecology.

Bringing Communities Together: Mapping the Investigation-Study-Assay-Model (ISA) to Fast Healthcare Interoperability Resources (FHIR).

Adhering to FAIR principles (findability, accessibility, interoperability, reusability) ensures sustainability and reliable exchange of data and metadata. Research communities need common infrastructures and information models to collect, store, manage and work with data and metadata. The German initiative NFDI4Health created a metadata schema and an infrastructure integrating existing platforms based on different information models and standards. To ensure system compatibility and enhance data integration possibilities, we mapped the Investigation-Study-Assay (ISA) model to Fast Healthcare Interoperability Resources (FHIR). We present the mapping in FHIR logical models, a resulting FHIR resources' network and challenges that we encountered. Challenges mainly related to ISA's genericness, and to different structures and datatypes used in ISA and FHIR. Mapping ISA to FHIR is feasible but requires further analyses of example data and adaptations to better specify target FHIR elements, and enable possible automatized conversions from ISA to FHIR.

P651 A randomised controlled trial of IBD-BOOST, a digital cognitive behavioural self-management programme for fatigue, and/or pain, and/or faecal incontinence in inflammatory bowel disease

Abstract Background Many people with IBD experience fatigue, pain and faecal incontinence (FI), impacting quality of life (QoL). We developed an interactive digital online self-management intervention (IBD-BOOST) based on a theoretically informed logic model and cognitive behavioural techniques, to treat these symptoms. We aimed to assess if IBD-BOOST, alongside health care professional (HCP) support and care as usual (CAU), provides greater relief from symptoms and improvement of QoL compared with CAU alone. Methods We conducted a pragmatic multi-centre two-arm parallel group randomised controlled trial (RCT) recruiting patients from clinics and national registries who rated the impact of fatigue and/or pain and/or FI as ≥5/10. Those in the IBD-BOOST arm received 6 months access to the 12-session IBD-BOOST programme, a 30-minute telephone support call with a trained HCP after session 1 and weekly in-site email messages from the HCP for 3 months. The UK Inflammatory Bowel Disease Questionnaire (UK-IBDQ) and global rating of symptom relief (GRSR) at 6 months were dual primary outcomes (alpha=0.025 was considered statistically significant as there were two primary outcomes). Other secondary outcomes, including individual symptoms, were measured at 6 and 12 months. Complier-averaged causal effects (CACE), sensitivity and pre-specified subgroup analyses were conducted. Results 780 participants were randomised (Figure 1), 432 with Crohn’s disease, 348 with ulcerative colitis or other IBD; 520 (66.7%) were female, mean age 49 years. At 6 months, both UK-IBDQ and GRSR were similar between the BOOST and CAU arms, p=0.19 for IBDQ and p=0.39 for GRSR (Table 1). Adverse events were similar between groups. FI score and EQ5D utility score (secondary outcomes) were significantly in favour of IBD-BOOST at 6 months, but pain and fatigue were no different. 57% of the intervention group completed a pre-defined minimum “dose” of 4 sessions (i.e. compliers). The CACE analysis suggested that compliers were more likely to report better QoL (p=0.03). Subgroup analysis of those meeting criteria for irritable bowel syndrome (IBS), showed that IBD-BOOST was more effective in improving IBDQ and GRSR at 6 months when compared with CAU for the IBD-IBS group (pinteraction=0.015 and 0.046, respectively). Conclusion This large RCT found that IBD-BOOST did not improve IBDQ and GRSR in patients with IBD and fatigue and/or pain and/or FI relative to CAU. Participants reported less FI at 6 and 12 months. Those who complied with the intervention showed a more pronounced IBDQ treatment effect. Those with IBD-IBS at baseline improved more than those without, suggesting a possible for further effectiveness research on IBD-BOOST. Trial registration: ISRCTN71618461

A structured review of start-up accelerator performance measurement: an integrated entrepreneurial program evaluation approach

ABSTRACT As a distinct type of early-stage entrepreneurial support organization, start-up accelerators are theoretically well positioned as a new and burgeoning phenomenon for fostering the process of new venture creation. The rapid expansion and notoriety of these intermediaries combined with a growing list of well-known high growth companies emerging from their programs hints at their potential impact. Yet, the question of whether accelerators work (or not) and to what effect is still at a formative stage. The objective of this paper is to conduct a structured review of what accelerators ‘do’ and how scholars have chosen to measure performance across various research designs, change variables and multiple levels of analysis. Drawing from program evaluation theory, an integrated entrepreneurial logic model is used to capture and sort variables associated with measuring start up accelerator performance between 2011 and 2021. We make several contributions through our analysis of research designs, linked change variables and thematic areas to provide insight into the advances, gaps, limitations and tensions arising from extant scholarly attempts at SA performance measurement. The developmental impact of SA programs is discussed with methodological, theoretical, and practical implications for documenting progress and future research pathways charted.

Application of a new fuzzy logic model known as "SMRGT" for estimating flow coefficient rate

Since we all have our own set of limitations when it comes to perceiving the world and reasoning profoundly, we are constantly met with uncertainty as a result of a lack of information (lexical impression, incompleteness), as well as specific measurement inaccuracies. It has been found that uncertainty, which shows up as ambiguity, is the root cause of complexity, which is everywhere in the real world. Most of the uncertainty in civil engineering systems comes from the fact that the constraints (parameters) are hard to understand and are described in a vague way. The ambiguity comes from a number of sources, including physical arbitrariness, statistical uncertainty due to using limited information to estimate these characteristics, and model uncertainty due to using overly simplified methods and idealized depictions of actual performances. Thus, it is better to combine fuzzy set theory and fuzzy logic. Fuzzy logic is well-suited to modelling the indeterminacy and ambiguity that results from multiple factors and a lack of data. In order to improve upon a previous predictive model, this paper uses a smart model built on a fuzzy logic system (FLS). Precipitation, temperature, humidity, slope, and land use data were all taken into account as input variables in the fuzzy model. Toprak's original explanation of the simple membership function and fuzzy rules generation technique (SMRGT) was based on the fuzzy-Mamdani methodology and used the flow coefficient as its output. The model's results were compared to available data. The following factors were considered in the comparison: 1) The maximum, minimum, mean, standard deviation, skewness, variation, and correlation coefficients are the seven statistical parameters. 2) Four types of error criteria: Mean Absolute Relative Error (MARE), Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). 3) Scatter diagram.

Effective Steps in Growing Listening Skill

The aim of this research are to describe effective steps listening skill teaching of grade first students in University of Siliwangi Tasikmalaya, west java, Indonesia. This research is treatment test again conclusion of research result with title of “Increasing Lecturing Logic-Based Learning Improves Listening Skills” of Agustus 2022.The method used in this study is experimental research. Besides the technique of data collecting was taken by examination and observation. The research held for student in 1st semester of 2022/2023 academic year at Faculty of Teacher Training and Education University of Siliwangi Tasikmalaya. The data were processed quantitatively. The result know that steps listening skills teaching based on the logic model have a positive impact toward student listening skill. To stretcher this research result it is suggested that the research should be followed up and validated by whom in the same profesion.