Proactive Model Research Articles

Development of proactive care model for patients with chronic kidney disease stage 4–5 to clinical outcomes and quality of life: an action research

ABSTRACT Background Patients with chronic kidney disease often struggle to control clinical symptoms and need help from the healthcare team. This study aimed to develop a proactive care model for stage 4–5 chronic kidney disease patients and investigate its effectiveness on their clinical outcomes and quality of life in Thailand. Methods The study was a comprehensive, collaborative effort conducted in North Thailand involving a multidisciplinary team of healthcare professionals. This team, which included physicians, professional nurses, pharmacists, and nutritionists from the Chronic Kidney Disease Clinic at a secondary hospital, worked together to develop and implement a proactive care model for stage 4–5 chronic kidney disease patients. The research instruments used were a proactive care model for CKD stages 4–5 patients, the clinical outcomes assessment form, and the Kidney Disease Quality of Life Short Form. Quantitative data were analyzed using descriptive statistics, Chi-Square, and dependent t-tests, while qualitative data were analyzed using content analysis. Results The proactive care model for patients with chronic kidney disease stage 4–5 consists of 1) a multidisciplinary team providing chronic kidney disease standards, 2) providing knowledge and counseling for behavior change, and 3) supporting self-management of patients with chronic kidney disease. After the experiment, mean systolic blood pressure, diastolic blood pressure, and mean potassium were significantly lower than before, and Hematocrit significantly increased. In contrast, glomerular rate, fasting blood sugar, and hemoglobin A1C did not change after the intervention compared to before (p > .05). After the experiment, patients’ overall quality of life significantly increased. Conclusions This study demonstrated that the proactive care model for Chronic Kidney Disease stage 4–5 patients significantly improved clinical outcomes and profoundly impacted quality of life. Therefore, all components of the proactive care model should be applied, including working as a multidisciplinary team and helping patients adjust their behaviors and manage their health.

When feeling safe is not enough to voice: Is emotional energy the missing key?

Organizations often seek ways to motivate their employees to make positive contributions. Drawing on the proactive motivational model and social information processing (SIP) theory, this study aims to explore the effects of hotel employees’ emotional energy on supportive and constructive voice and the moderating role of psychological safety climate. The proposed model was tested using data collected from hospitality and tourism employees through Prolific (n = 390). The results indicate that emotional energy has a direct positive influence on supportive and constructive voice, and psychological safety climate moderates the relationship between emotional energy and constructive voice but not supportive voice. This research contributes to the voice behavior literature by looking at specific dimensions of voice behavior and provides additional empirical evidence for the proactive motivational model.

Deep-learning and data-resampling: A novel approach to predict cyanobacterial alert levels in a reservoir

The proliferation of harmful algal blooms results in adverse impacts on aquatic ecosystems and public health. Early warning system monitors algal bloom occurrences and provides management strategies for promptly addressing high-concentration algal blooms following their occurrence. In this study, we aimed to develop a proactive prediction model for cyanobacterial alert levels to enable efficient decision-making in management practices. We utilized 11 years of water quality, hydrodynamic, and meteorological data from a reservoir that experiences frequent harmful cyanobacterial blooms in summer. We used these data to construct a deep-learning model, specifically a 1D convolution neural network (1D-CNN) model, to predict cyanobacterial alert levels one week in advance. However, the collected distribution of algal alert levels was imbalanced, leading to the biased training of data-driven models and performance degradation in model predictions. Therefore, an adaptive synthetic sampling method was applied to address the imbalance in the minority class data and improve the predictive performance of the 1D-CNN. The adaptive synthetic sampling method resolved the imbalance in the data during the training phase by incorporating an additional 156 and 196 data points for the caution and warning levels, respectively. The selected optimal 1D-CNN model with a filter size of 5 and comprising 16 filters achieved training and testing prediction accuracies of 97.3% and 85.0%, respectively. During the test phase, the prediction accuracies for each algal alert level (L-0, L-1, and L-2) were 89.9%, 79.2%, and 71.4%, respectively, indicating reasonably consistent predictive results for all three alert levels. Therefore, the use of synthetic data addressed data imbalances and enhanced the predictive performance of the data-driven model. The reliable forecasts produced by the improved model can support the development of management strategies to mitigate harmful algal blooms in reservoirs and can aid in building an early warning system to facilitate effective responses.

Impact of Reverse Empowerment and Proactive Motivations on Physicians' Online Knowledge Sharing in Digital Platforms: Survey Study.

Digital platforms offer a venue for patients and physicians to exchange health information and provide health care services outside traditional organizational contexts. Previous studies have seldom focused on the factors that drive the proactivity of physicians' online behavior. Additionally, there is limited research exploring the influence of patients on physicians' online behavior, particularly from the perspective of patients possessing power. This study aims to investigate the effect of patient-empowering behaviors on physicians' online knowledge sharing and uncover the potential mechanisms of proactivity. Based on the proactive motivational model and empowerment theory, we propose the existence of a reverse empowerment process, where empowerment flows from patients to physicians. We suggest that patient-empowering behaviors may drive physicians' online knowledge sharing as a form of proactivity. Specifically, 3 proactive motivational states-knowledge-sharing self-efficacy, sharing meaning, and positive professional affect-mediate this relationship. Additionally, platform extrinsic rewards, as a contextual factor, have a moderating effect. To validate our proposed research model, we conducted a survey in China using the WJX platform, targeting physicians engaged in online knowledge sharing. The measurement instrument utilizes validated items adapted from prior research, using a 5-point Likert scale. We collected 257 valid responses, ensuring that participation was both anonymous and voluntary. Data analysis was performed in 2 stages. The first stage assessed the measurement model for reliability and validity, using the Harman 1-factor test and confirmatory factor analysis. The second stage used partial least squares-structural equation modeling to examine the direct, moderation, and mediation effects among the constructs, with bootstrapping used for significance testing. This comprehensive approach ensures a robust analysis of the proposed hypotheses and contributes to the overall validity of our research model. Perceived patient-empowering behaviors significantly and positively influence physicians' online knowledge sharing (β=0.27, P<.001). Knowledge-sharing self-efficacy (effect=0.06, P=.04), sharing meaning (effect=0.12, P<.001), and positive professional affect (effect=0.10, P=.003) each partially mediate the effect of patient-empowering behaviors on physicians' online knowledge sharing. The overall proactive motivational states play a complete mediation role, meaning the entire indirect effect of the model is significant (effect=0.29, P<.001), while the direct effect in the model is nonsignificant (effect=0.07, P=.26). Additionally, platform extrinsic rewards significantly and negatively moderate the effect of sharing meaning on physicians' online knowledge sharing (β=-0.13, P=.001). This study is the first to recognize and examine proactivity as an alternative mediating mechanism for physicians' online knowledge sharing, highlighting the active role of patients in empowering physicians. It makes a significant contribution to the existing literature on empowerment, eHealth, and proactive behavior. Additionally, the findings offer valuable guidance for designing and managing digital platforms to ensure service sustainability.

A proactive opportunistic maintenance decision model based on reliability in train systems

This paper presents an innovative framework for optimizing maintenance strategy in complex electromechanical systems (CEMS), by mitigating over-maintenance and suboptimal resource allocation. This study proposes an optimized maintenance strategy based on opportunistic maintenance with reliability as the core. A topological network model is used to establish a model that forecasts system reliability from function and structure, resulting in the identification of critical components in the system. Subsequently, a maintenance model for mechanical, electrical and information components is proposed and a method for determining maintenance reliability thresholds is suggested. The research imposes constraints in component and system reliability, in constructing an opportunistic maintenance strategies model. The objective of this model was to minimize the maintenance cost. A case study on high-speed railway bogie system supports this strategy, effectively guaranteeing system reliability while reducing maintenance costs. Maintenance resources are allocated reasonably across various stages, thereby reducing wastage of resources.

Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory

In the context of integrating renewable energy sources such as wind and solar energy sources into distribution networks, this paper proposes a proactive low-carbon dispatch model for active distribution networks based on carbon flow calculation theory. This model aims to achieve accurate carbon measurement across all operational aspects of distribution networks, reduce their carbon emissions through controlling unit operations, and ensure stable and safe operation. First, we propose a method for measuring carbon emission intensity on the source and network sides of active distribution networks with network losses, allowing for the calculation of total carbon emissions throughout the operation of networks and their equipment. Next, based on the carbon flow distribution of distribution networks, we construct a low-carbon dispatch model and formulate its optimization problem within a Markov Decision Process framework. We improve the Soft Actor–Critic (SAC) algorithm by adopting a Gaussian-distribution-based reward function to train and deploy agents for optimal low-carbon dispatch. Finally, the effectiveness of the proposed model and the superiority of the improved algorithm are demonstrated using a modified IEEE 33-bus distribution network test case.

Seeking for better performance: a synergy effect of algorithmic and leader management transparency on employee knowledge seeking behaviour and performance in China

ABSTRACT Existing research has revealed the mixed effects of algorithmic management transparency. By differentiating the source of transparency, we aim to understand when and how algorithmic management transparency translates into gig workers’ performance. Drawing upon the proactive motivation model, we propose that the effects of algorithmic management transparency hinge on leader management transparency to influence gig workers’ motivational states, which promote their knowledge seeking and performance. Based on multi – source and time – lagged data from 219 gig workers in China, the model was supported. We highlight the synergistic effect of algorithmic and leader management transparency on facilitating gig workers’ proactivity to improve performance.

From inner needs to external actions: the impact of face consciousness on tourists’ pro-environmental behavior

ABSTRACT Research on tourists’ pro-environmental behavior has been receiving increasing attention, yet there remains a significant gap in understanding how to ensure the sustainability of such behavior. Drawing on the proactive motivation model, this study examines the influence of face consciousness on tourists’ pro-environmental behavior in China. Structural equation modeling analyzed data from 598 tourists in Chengdu. Findings show that gaining face promotes tourists’ pro-environmental behavior. Moreover, green self-efficacy, perceived insider status, and harmonious environmental passion mediate the relationship between face consciousness and tourists’ pro-environmental behavior. Sincere social interaction enhances the positive influence of gaining face through perceived insider status and harmonious environmental passion on tourists’ pro-environmental behavior. This study enriches the existing literature on the relationship between face consciousness and tourists’ pro-environmental behavior, and also provides management basis for promoting tourists’ sustained engagement in pro-environmental behavior.

Would entitled people be more proactive at work? Examining contrasting mechanisms based on the proactive motivation model

Previous research has largely regarded psychological entitlement as detrimental to organizational effectiveness. In contrast, this study presents a more nuanced view by exploring its relationship with proactive work behavior. Drawing on the proactive motivation model, we propose that psychological entitlement influences proactive work behavior in both positive and negative ways, with need for uniqueness, role-breadth self-efficacy, and emotional exhaustion serving as mediators for the “reason to,” “can do,” and “energized to” states, respectively. Through a field study and an experiment, our results consistently showed that psychological entitlement had positive influences on proactive work behavior. Our study thus highlights that psychological entitlement should not be seen solely as problematic, sheds light on new mechanisms through which it influences work outcomes, and deepens our understanding of individual differences in proactive behavior at work.

Fairness matters for change: A multilevel study on organizational change fairness, proactive motivation, and change-oriented OCB.

The success of organizational change often hinges on the perception of fairness within a change unit. This group-level organizational change fairness is crucial for enhancing proactive motivation states and fostering positive change-oriented organizational citizenship behavior (OCB). Rooted in the proactive motivation model, this study establishes a comprehensive multilevel framework to investigate the influence of group-level organizational change fairness on employees' change-oriented OCB. It explores the mediating role of three proactive motivational states and the moderating impact of perceived change impact. Analyzing data collected from 597 employees within 107 teams across 43 Chinese companies, our findings indicate that group-level perceived organizational change fairness significantly predicts employees' change-oriented OCB through organizational change self-efficacy, involvement, and positive emotional experiences. Furthermore, the study reveals that group-level perceived change impact moderates the relationship between group-level fairness perception and both change self-efficacy and positive emotional experiences, with stronger associations observed under conditions of low perceived change impact. These insights notably advance our understanding of the cross-level determinants influencing change-oriented OCB through perceived fairness and proactive motivation. Managers should focus on developing fairness perceptions to stimulate OCB by fostering employees' proactive motivation states, particularly during low-impact organizational changes. Our findings provide valuable implications for organizational change management practices.

Using Panel Management to Identify Adult Patients With High-Risk Metabolic Dysfunction-Associated Steatotic Liver Disease/Metabolic Dysfunction-Associated Steatohepatitis Fibrosis in a Primary Care Clinic: A Pilot Study.

As rates of metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) rise, national organizations have released new guidance for primary care-driven detection of patients with advanced fibrosis who are most likely to have clinically relevant morbidity. Yet time constraints, workflow, and practitioner awareness limit integration of risk identification into clinical care. At the authors' primary care clinic, they implemented a panel management strategy that utilized the electronic health record to identify patients older than 35 years of age at risk for MASLD fibrosis with abnormal Fibrosis-4 (Fib-4) scores. Using a proactive model, these patients were offered elastography-based screening and follow-up appointments focused on metabolic health, with referrals to subspecialty care when indicated. Of 855 patients older than 35 years of age, 384 were identified as having risk factors for MASLD/MASH. Of these, 53 had abnormal Fib-4 scores with no prior work-up; 29 patients consented to a shear wave elastography; 16 underwent shear wave elastography; and 6 had moderate or high results concerning for at-risk fibrosis. Twenty patients attended MASLD-focused appointments. Reluctance to pursue testing was driven by skepticism surrounding preventative medicine, perceived cost, and desire to focus on other medical problems, some of which were life-limiting. Panel management represents a scalable strategy to quickly identify patients in primary care most likely to experience complications from MASLD/MASH and provides a targeted intervention to direct further management. Limitations include access to care, medical complexity, and patient acceptance.

Construction of an Adolescent Spinal Health Intelligent Early Warning and Intervention System under the Proactive Health Model

This study explores the construction of an intelligent early warning and intervention system for adolescent spinal health and its application under a proactive health model. The research demonstrates that through real-time monitoring and personalized interventions, the Spinal Health Index of adolescents in the intervention group significantly improved, increasing from 65 to 85 points, while the control group's index slightly decreased from 60 to 58 points. Personalized intervention strategies, such as the combination of exercise and nutritional interventions, were found to be the most effective, indicating that daily activity levels significantly impact spinal health. The system plays a crucial role in the management of adolescent spinal health by facilitating real-time monitoring, personalized interventions, and modifications in health behaviors. Despite limitations including constraints in sample size and geographical scope, a relatively short intervention period, and insufficient data diversity, future research can enhance universality and model generalizability by expanding sample sizes, prolonging the intervention period, and increasing data diversity. Looking forward, the integration of multimodal data, optimization of the user interface, and establishment of long-term tracking mechanisms will further enhance system performance and promote the improvement of spinal health management in adolescents.

A Linear Swarm – Based Intelligence for Resource Allocation and Fault Prediction in Cloud

The widespread utilization Cloud Computing (CC) services for hosting real-time applications have led to the appearance of service dependability as an essential issue in both users and Cloud Service Providers (CSP). Two diverse fault tolerant approaches are provided to improve the cloud service reliability known as proactive and reactive model. Various prevailing approaches consider the coordination problem among the Virtual Machines (VMs) executes parallel processing. Devoid of appropriate VM coordination, the parallel processing outcomes are not so appropriate. To handle this issue, a VM-based cluster allocation model is designed to diminish the total resource consumption by the data centers and network resources. Here, the VM migration process is performed for deteriorating PM to certain optimal PMs. At last, the optimal target selection is handled with an improved optimization approach known as linear Swarm-based intelligence. Here, various metrics are evaluated and compared with other approaches. The experimental outcomes illustrate the efficiency of the anticipated model.

Recruitment and retention of new doctors in remote and Aboriginal medical services through the Remote Vocational Training Scheme's Targeted Recruitment Strategy: a focus group study.

To explore the results of a targeted recruitment strategy designed to attract and retain new doctors in remote and Aboriginal medical services where they can access Remote Vocational Training Scheme (RVTS) training and support to qualify as general practitioners. Two 2-hour purposeful online focus groups on the RVTS' Targeted Recruitment Strategy. Five participants and nine stakeholders with knowledge and experience of the strategy. Data were transcribed and deductively and inductively coded for themes including insights from separate project reference and stakeholder advisory groups. Perspectives of the strategy. The overarching theme was that the strategy is useful for attracting and retaining new general practitioners in areas of high need and is potentially scalable. Since 2018, 20 new doctors were recruited via the Targeted Recruitment Strategy and six of them completed the RVTS program. The strategy could better differentiate target locations because increasing communities are experiencing major general practice workforce shortages. The package of employment and training could also be more clearly defined for participants, nationally marketed and collaboratively implemented. Further, more site accreditation and ongoing risk and quality review is needed, along with intense early supports for participants who are new to both the community and general practice work. The Targeted Recruitment Strategy is still maturing but the early results suggest it is a unique and proactive model for attracting and improving access to general practitioners in places with high needs. It could be strengthened through formal agreements between communities and agencies, ensuring coordinated implementation, clarifying roles and responsibilities, and developing clear pathways for risk and quality management.

A Mixed-Methods Study to Evaluate Depression and Anxiety Among Undergraduate Nursing Students.

As the backbone of future healthcare delivery, the mental health of undergraduate nursing students can have a direct impact on patients' perceptions of health and the quality of their healthcare learning. However, there has been little qualitative research on the factors influencing the mental health of undergraduate nursing students. This study investigated the current status and factors influencing depression and anxiety in undergraduate nursing students. Questionnaires were administered to third-year nursing students at a medical college university using the Self-Assessment Scale for Anxiety (SAS) and the Self-Depression Scale (SDS). The students who suffered from anxiety or depression were selected for qualitative interviews and the results of the interviews were analysed by using the seven-step Koretz analysis. (1) SAS and SDS scores of undergraduate nursing students were higher than the national average. (2) The qualitative interviews revealed that five themes, including childhood experience, body image, interpersonal relationships, professional identity and career planning, influenced mental health. Undergraduate nursing students exhibited a high prevalence of anxiety and depression. The colleges should establish a proactive educational model centred on teaching quality, professional identity, career planning and mental health. This study found that the mental health of undergraduate nursing students was influenced by a variety of factors, which require diverse and constant interventions. School, family and social resources should collaborate to develop a systematic approach for better mental health education in undergraduate nursing students.

Цифровая трансформация здравоохранения: технологические и организационные аспекты

The article shows that the digital transformation of domestic healthcare leads to the emergence of new opportunities for its qualitative development and improvement of the efficiency of services provided, in this regard, such a transformation is one of the key directions of the national Healthcare project implemented in the Russian Federation. It shows how telemedicine, electronic medical records, artificial intelligence, big data analysis, etc. They provide an increase in the efficiency of medical services, allowing the formation of technological and organizational aspects for the effective development of the healthcare system within the framework of the so-called "4p medicine", which largely embodies the paradigm of its development in the digital age. The emergence of 4P medicine represents a transition from a reactive to a proactive model of the healthcare system, focusing on prevention, early detection and personalized treatment in order to improve the effectiveness of the healthcare system and reduce the cost of its operation.

Shift-left in your fight against supply chain fraud

Supply chain operations rely upon various supply chain software systems, centred around the enterprise resource planning (ERP) system. Traditional and still widely utilised and relied-upon audit techniques have addressed the issue of finding problems — particularly fraud — typically too late after they have happened. Inasmuch as the ERP system is considered the business system of record, it may not be the system of origination for every supply chain transaction, meaning that fraud examination needs to look beyond the ERP system and notably before the ERP general ledger. The closer to a transaction’s point of origin the unusual behaviour can be detected, the sooner the outlier issue can be fixed, and ideally averted in the future with the right corrective actions taken then and there. This all helps to prevent the problem — the bad data, the offending transaction, and possibly the incorrect goods — from travelling through the supply chain and manifesting into something worse. Ramifications can include knock-on effects to data analysis, impacts to decisions, passing bad information to supply chain partners, conveying the wrong goods and material effects on financial statements. This paper presents a supply chain perspective that reveals just how much control an enterprise really does have over its supply chain transactions, showcases how reactive fraud discovery methodologies remain firmly in place, and offers a more proactive business model that leverages what most companies already have to improve internal controls and decrease incidences of fraud.

Implementation and evaluation of a proactive consultation-liaison model on an inpatient obstetric unit

ObjectivePerinatal mental and anxiety disorders (PMADs) contribute to adverse health outcomes, though they are underrecognized and undertreated. Inpatient obstetric settings represent a unique opportunity for behavioral health engagement, including screening, brief treatment, and referrals for outpatient care. The proactive consultation-liaison (CL) model has proven effective in general hospital settings but is not well-studied in obstetric settings. This article describes the implementation and evaluation of a proactive CL model in an inpatient obstetric unit within a tertiary medical center. MethodsWe implemented a multidisciplinary, proactive CL model in an inpatient obstetric unit with the purpose of identifying patients at risk for or experiencing PMADs and providing intervention and/or referral to treatment. Systematic screening of 7322 admitted patients was performed over a 17-month period to identify eligible patients for behavioral health consultation. Consultation data was retrospectively extracted from the electronic medical record. Key implementation outcomes were assessed using a RE-AIM measures (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework. Results1589 initial consults were conducted by the multidisciplinary team, yielding a consult rate of 21.7 %. The majority of consults (94 %) were completed by a social worker or psychologist, with most patients identified for consultation at multidisciplinary rounds (60.7 %). The most common indications for consultation with a psychiatrist included medication management, history of bipolar disorder, and history of anxiety. All invited staff and providers participated in the model. Alternative funding sources agreed to cover the salaries of the multidisciplinary team following conclusion of pilot grant funding. ConclusionsA proactive CL model implemented in an inpatient obstetric unit led to a higher consult rate (21.7 %) than is observed with traditional CL services. A multidisciplinary proactive CL model shows promise in identifying people at-risk for PMADs and providing targeted interventions to prevent PMADs and treat those with active symptoms.

Study examining the significant role of intellectual property protection in driving radical technological innovation among national research project teams, employing PLS-SEM and ANN modeling.

This study examines the role of intellectual property protection (IPP) in enhancing radical technological innovation (RTI) within national research project teams, using an innovation-driven theory and an ability-motivation-opportunity (AMO) perspective. This study utilizes a sample of 336 national research project team members from various Chinese universities, research institutes, and corporations to analyze the theoretical model. Additionally, a two-stage hybrid partial least squares structural equation modeling (PLS-SEM) approach, combined with artificial neural network techniques (ANN), is employed to evaluate the hypotheses. The empirical findings of this study reveal a positive association between the intensity of IPP and RTI within national research project teams. Research and development investment intensity (R&DII) is identified as the primary predictor, while integrated leadership (IL) and group potential (GP) play crucial moderating roles. These groundbreaking findings extend the scope of innovation-driven and AMO theories, providing a proactive model for national research project teams to propose improvements to the IPP system, ultimately enhancing the realization of RTI.

Analysis of the Management of Protected Areas in Galapagos: 60 Years after Its Declaration as a National Park

The Galapagos Islands are one of the most emblematic protected areas on the planet and also one of the most studied. Their main economic activity is tourism, which has grown rapidly in recent years. The increase in tourists is associated with the increase in population and the introduction of invasive species, which puts conservation at risk. This makes adequate management even more necessary and relevant on an international scale since the economy of Galapagos depends on the good state of conservation of its ecosystems and species. Numerous studies have shown that social factors, rather than physical–natural ones, determine the success or failure of a marine protected area (MPA), so they must be urgently incorporated into all phases of MPA management. In this study, we analyzed the management of the protected areas of Galapagos, including the National Park and the Marine Reserve, with the purpose of highlighting their weaknesses and strengths. The methodology used presents five prescribed scenarios in which priority is given to one or other factors, each of which has an impact on the biophysical and socio-economic systems that are influenced by the MPA. These scenarios were called proactive, interactive, centralized, learning, and formal management. Data were obtained through a comprehensive literature review and primary data collection methods, including in-depth interviews with key stakeholders and DPNG managers, as well as direct observations. Interviewees covered the main relevant sectors in the archipelago (fishing, tourism, and conservation). The results show that the archipelago’s protected areas follow a proactive management model with a continuous feedback loop. This feedback is a key element in any adaptive management process, which also allows practitioners to anticipate future problems. Both public participation and strategic planning are essential.