Proactive Model Research Articles

Preparing to succeed: the impact of an access module

ABSTRACT This article explores the sustained impact of an Access module on mature students from disadvantaged backgrounds as they progress to undergraduate study. Framed in the context of OfS concerns about attrition and conceptualisation of dispositional, situational and institutional barriers, student survey and tutor focus group data were collected in a case study at the UK’s largest distance learning university. Findings suggested an Access module impacted by aligning learner confidence with improved academic writing, provided a crucial preparatory starting point, and led to enhancements in student time management and competence in learning online. Institutional barriers were mitigated through a 1–1 proactive tutoring model. Recommendations about the implications for improving support for Year 1 students are made.

Rheumatoid arthritis, cardiometabolic comorbidities, and related conditions: need to take action.

Rheumatoid Arthritis (RA) is associated with an increased risk of cardiovascular disease and mortality, however, traditional cardiovascular risk factors do not fully explain this relationship. This high risk of cardiovascular morbidity and mortality in RA has been increasingly acknowledged in past decades, with accumulating evidence that RA is an independent cardiovascular risk factor; RA is also associated with metabolic syndrome, which correlates with disease activity, contributing to the increased prevalence of coronary heart disease in RA patients. Moreover, multimorbidity, including the presence of long-term conditions, impacts adverse clinical outcomes in RA patients, emphasizing the need for holistic management that requires an understanding of shared pathophysiological mechanisms, such as systemic inflammation and immune dysregulation. For all these reasons, the management of RA patients with cardiometabolic comorbidities is a complex endeavor that requires a patient-centered, multidisciplinary approach. In this sense, there is a need to re-evaluate the approach toward a proactive model of care, moving away from a reactive medical paradigm to a multidimensional integrated management model, including aggressive screening, preventive strategies, and tailored therapeutic interventions. The aim of this review was to thoroughly review the literature on cardiometabolic comorbidities and related conditions linked to RA to enable us to identify the necessary actions required to effectively tackle the increasing burden of illness from a fully comprehensive perspective.

How does managerial coaching influence knowledge sharing in the workplace? A perspective of proactive motivation model

Purpose Drawing upon the Proactive Motivation Model, this study aims to investigate the relationship between managerial coaching and employee knowledge-sharing behavior via psychological safety and learning goal orientation. This study also proposes that employee psychological safety and learning goal orientation sequentially mediate the relationship between managerial coaching and employee knowledge-sharing behavior. Design/methodology/approach This study used a time-lagged quantitative research design to test the proposed hypotheses. Using a self-administered questionnaire, data was gathered from 220 employees of information technology companies in Pakistan. This study used Partial Least Squares Structural Equation Modeling (PLS-SEM) two-stage approach to test the measurement and structural models. Findings The findings of the study support that there is an indirect relationship between managerial coaching and employees’ knowledge-sharing behaviors via psychological safety and learning goal orientation. In addition, the findings also support the sequential mediation of psychological safety and learning goal orientation in the proposed model. Practical implications The results of this study highlight that managers can play a vital role in fostering proactive resource-sharing behaviors of employees in knowledge-intensive organizations. Originality/value There is limited research on the relationship between managerial coaching and employees’ knowledge-sharing behavior. This study has analyzed this relationship using a motivational perspective. It makes important theoretical contributions by investigating the mechanisms through which managerial coaching influences employees’ knowledge-sharing behavior in organizations.

Virtues as protective factors for adolescent mental health.

This paper explores the decline in adolescent mental health and the weakening of traditional moral frameworks, positing education in the virtues as protective of mental health due to the intrinsic link between moral/existential wellbeing and psychological health. By integrating character education into school curricula, a continuous "dosage" of moral guidance may be an optimal way to ensure a gradual and ever-clearer articulation of a life worth living and how to live well. The paper critiques popular clinical and positive psychological approaches to promoting wellbeing, which often miss the existential and moral dimensions of adolescent growth. The conclusion emphasizes the need for integrating moral education into mental health interventions to address the comprehensive existential and moral dimensions of adolescent development. This paper advocates for a proactive character developmental model that nurtures moral and existential growth, recognizing challenges with virtue and meta-virtue development as integral to personal and moral evolution, and enhancing the moral and psychological fortitude of adolescents.

A realist evaluation protocol: assessing the effectiveness of a rapid response team model for mental state deterioration in acute hospitals.

Mental state deterioration poses significant challenges in healthcare, impacting patients and providers. Symptoms like confusion and agitation can lead to prolonged hospital stays, increased costs, and the use of restrictive interventions. Despite its prevalence, there's a lack of consensus on effective practices for managing mental state deterioration in acute hospital settings. To address this gap, a rapid response team model has been proposed as a potential intervention, aiming to provide early identification and targeted interventions. Based on realist evaluation steps, first, initial program theories are formulated to understand the logic behind the intervention. Second, literature synthesis identifies empirical evidence on contexts, mechanisms, and outcomes elements, refining initial theories. During the third step, data will be collected using qualitative methods such as field observations and interviews, as well as quantitative methods such as surveys of the staff, audits of electronic medical records, and analysis of incident records of mental state deterioration. Analysing this data informs configurations of contexts, mechanisms, and outcomes. In the fifth step, the configurations are synthesised, presenting refined, evidence-informed program theories. This study addresses the knowledge gap by evaluating the rapid response model's effectiveness in managing mental state deterioration in acute hospital settings. Realist principles guide the exploration of causal mechanisms and their interaction with specific implementation contexts. The objective is to identify what works, for whom, and under what circumstances, aiming to manage deterioration, reduce restrictive interventions, and enhance the experience for patients and staff by implementing a proactive model of care. The findings contribute to evidence-based approaches for managing mental state deterioration in hospital settings, informing policy and practice in this crucial area of healthcare.

A community proactive health management model for family doctors in Shandong, China.

Background Chronic disease management is an essential part of public health management and a fundamental means of slowing down the progression of major diseases. Unlike traditional medical care, proactive health management focuses on the combination of prevention and treatment. Proactive health management can reduce the re-admission rate of patients with chronic diseases, improve long-term prognosis, and effectively reduce the disability and mortality rates of major health events. This study aimed to establish a proactive health management model based on a grid-based medical service team arrangement, and to explore the effect of this model on managing chronic diseases in community health service institutions. Methods A grid-based medical service team was established in Binzhou, Shandong, China, consisting of general practitioners, nurses, public health doctors, health promoters and community grid liaison staff. Each team was responsible for several areas to monitor critical populations within the grid and compile statistics on the health management of these key populations in 2022-2023. Results A total of 2050 patients with coronary artery disease, 4973 patients with hypertension, and 1621 patients with type 2 diabetes were followed up in 2022. Compared with 155,612 resident health records in 2022, the number of records increased by 140.50% in 2023. The number of patients with hypertension under health management in 2023 increased by 50.92%; patients with type 2 diabetes increased by 74.65%; and the number of coronary artery disease increased by 42.00%. After the implementation of grid management, the hospitalisation rate for patients with type 2 diabetes significantly decreased in 2023 (P P >0.05). Conclusion The grid-based community proactive health management model makes full use of the advantages of community resources and improves the pertinence and coverage of community health services. Moreover, it reduces hospitalisation for patients with type 2 diabetes.

Effects of Career Aspirations of New Generation Employees on Proactive Socialization Behavior

Organizational socialization of the new generation of employees has become a hot topic affecting the sustainable development of organizations. From the perspective of a proactive motivation model, the mechanism of career aspirations on new generation employees’ proactive socialization behaviors was explored. Three hundred three responses were gathered from an online survey. The results show that career aspirations were positively associated with proactive socialization behavior. Sense of hope played a mediating role in the relationship between career aspirations and proactive socialization behavior. Perceived organizational support moderated the relationship between career aspirations and a sense of hope. As employees’ perception of organizational support increases, the positive effect of career aspirations on a sense of hope is weakened. Our findings help to understand why new generation employees exhibit proactive socialization behavior and provide management insights for organizations to motivate new generation employees to take proactive socialization behaviors.

The Role of Occupational Self-Efficacy as a Mediator between Paradoxical Leadership and Job Crafting from the Lens of Proactive Motivation Theory

Purpose- By utilizing the theory of the proactive motivation model, the mediation effect of occupational self-efficacy between paradoxical leadership and job crafting has been examined in this study. Study Design/methodology/approach- This study adopted a temporally segregated research design for two time intervals, 1 month apart; we collected data from 422 employees from the telecom sector of Islamabad and Rawalpindi. Findings- Using SMART PLS-SEM, the findings showed that the relationship between paradoxical leadership and job crafting was mediated by occupational self-efficacy. The data supported the hypothesized relationships. Research Practical Implications- The study highlights the significance of employees’ proactive behavior providing practical insights for managers and identifies occupational elements by incorporating the model of proactive motivation theory. Organizations can undertake leadership training that is specifically designed to promote proactivity. Managers should create a balanced leadership and conducive environment to initiate occupational self-efficacy that can promote proactivity in job crafting. Originality/value- There is limited research on paradoxical leadership that can align with the proactivity of employees who have been efficacious at work. These findings offer insight into how employees' occupational self-efficacy can initiate the proactivity of job crafting in a dynamic environment.

Elevating Smart Manufacturing with a Unified Predictive Maintenance Platform: The Synergy between Data Warehousing, Apache Spark, and Machine Learning.

The transition to smart manufacturing introduces heightened complexity in regard to the machinery and equipment used within modern collaborative manufacturing landscapes, presenting significant risks associated with equipment failures. The core ambition of smart manufacturing is to elevate automation through the integration of state-of-the-art technologies, including artificial intelligence (AI), the Internet of Things (IoT), machine-to-machine (M2M) communication, cloud technology, and expansive big data analytics. This technological evolution underscores the necessity for advanced predictive maintenance strategies that proactively detect equipment anomalies before they escalate into costly downtime. Addressing this need, our research presents an end-to-end platform that merges the organizational capabilities of data warehousing with the computational efficiency of Apache Spark. This system adeptly manages voluminous time-series sensor data, leverages big data analytics for the seamless creation of machine learning models, and utilizes an Apache Spark-powered engine for the instantaneous processing of streaming data for fault detection. This comprehensive platform exemplifies a significant leap forward in smart manufacturing, offering a proactive maintenance model that enhances operational reliability and sustainability in the digital manufacturing era.

1945-LB: Evaluation of Proactive Targeting Models Used to Identify People with Diabetes at Risk of Being Uncontrolled during Participation in a Digital Diabetes Management Program (DDMP)

1945-LB: Evaluation of Proactive Targeting Models Used to Identify People with Diabetes at Risk of Being Uncontrolled during Participation in a Digital Diabetes Management Program (DDMP)

Proactive cyber fraud response: a comprehensive framework from detection to mitigation in banks

PurposeThis study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study is to de velop an innovative cyber fraud (CF) response system that effectively controls cyber threats, prioritizes fraud, detects early warning signs (EWS) and suggests mitigation measures.Design/methodology/approachThe methodology involves a detailed literature review on fraud identification, assessment methods, prevention techniques and a theoretical model for fraud prevention. Machine learning-based data analysis, using self-organizing maps, is used to assess the severity of CF dynamically and in real-time.FindingsFindings reveal the multifaceted nature of CF, emphasizing the need for tailored control measures and a shift from reactive to proactive mitigation. The study introduces a paradigm shift by viewing each CF as a unique “fraud event,” incorporating EWS as a proactive intervention. This innovative approach distinguishes the study, allowing for the efficient prioritization of CFs.Practical implicationsThe practical implications of such a study lie in its potential to enhance the banking sector’s resilience to cyber threats, safeguarding stability, reputation and overall risk management.Originality/valueThe originality stems from proposing a comprehensive framework that combines machine learning, EWS and a proactive mitigation model, addressing critical gaps in existing cyber security systems.

Proactive review for people with diabetes in hospital: a cluster randomised feasibility trial with process evaluation, protocol V3.1

BackgroundDiabetes inpatient specialist services vary across the country, with limited evidence to guide service delivery. Currently, referrals to diabetes inpatient specialists are usually ‘reactive’ after diabetes-related events have taken place, which are associated with an increased risk of morbidity/mortality and increased length of hospital stay. We propose that a proactive diabetes review model of care, delivered by diabetes inpatient specialist nurses, may contribute to the prevention of such diabetes-related events and result in a reduction in the risk of harm.MethodWe will conduct a cluster randomised feasibility study with process evaluation. The proactive diabetes review model (PDRM) is a complex intervention that focuses on the prevention of potentially modifiable diabetes-related harms. All eligible patients will receive a comprehensive, structured diabetes review that aims to identify and prevent potentially modifiable diabetes-related harms through utilising a standardised review structure. Reviews are undertaken by a diabetes inpatient specialist nurse within one working day of admission. This differs from usual care where patients are often only seen after diabetes-related harms have taken place. The trial duration will be approximately 32 weeks, with intervention delivery throughout. There will be an initial 8-week run-in phase, followed by a 24-week data collection phase. Eight wards will be equally randomised to either PDRM or usual care. Adult patients with a known diagnosis of diabetes admitted to an included ward will be eligible. Data collection will be limited to that typically collected as part of usual care. Data collected will include descriptive data at both the ward and patient level and glucose measures, such as frequency and results of capillary glucose testing, ketonaemia and hypoglycaemic events. The analysis aims to determine the fidelity and acceptability of the intervention and the feasibility of a future definitive trial. Whilst this study is primarily about trial feasibility, the findings of the process evaluation may lead to changes to both trial processes and modifications to the intervention. A qualitative process evaluation will be conducted in parallel to the trial. A minimum of 22 patients, nurses, doctors, and managers will be recruited with methods including direct non-participant observation and semi-structured interviews. The feasibility of a future definitive trial will be assessed by evaluating recruitment and randomisation processes, staffing resources and quality of available data.DiscussionThe aim of this cluster randomised feasibility trial with a process evaluation is to explore the feasibility of a definitive trial and identify appropriate outcome measures. If a trial is feasible and the effectiveness of PDRM can be evaluated, this could inform the future development of inpatient diabetes services nationally.Trial registrationUK Clinical Research Network, 51,167. ISRCTN, ISRCTN70402110. Registered on 21 February 2022.

Does proactive care in care homes improve survival? A quality improvement project

BackgroundNHS England’s ‘Enhanced Health in Care Homes’ specification aims to make the healthcare of care home residents more proactive. Primary care networks (PCNs) are contracted to provide this, but approaches...

Predictive Analysis for Personalized Machine: Leveraging Patient Data for Enhanced Healthcare

This research explores predictive analysis for personalized machine: leveraging patient data for enhanced healthcare. By leveraging the power of information and analytics, the healthcare industry can be driven towards a more patient-centric, proactive model that enhances outcomes and improve the overall quality of care. The objectives of the study are to: determine the significance and challenges of predictive analytics in healthcare, ascertain the data analytics techniques used in healthcare to enhance patient care, find out how predictive analytics can be applied for enhanced healthcare, and determine the ethical considerations associated with healthcare predictive analytics. This study employs the case study approach and experimental design. The study analyzes case studies of real-time deployment of predictive analytics models in healthcare centers, examines how these models enhance the healthcare delivery in those centers. Experiments were also conducted to understand how predictive analytics works. The C4.5 learning algorithm was employed to predict the presence of chronic kidney disease (CKD) in patients and differentiate between those not affected by the condition. The C4.5 classifier shows reasonable strength, evident in the large number of rightly classified occurrences (396) and a low misclassification of only 4 occurrences. This is further demonstrated by a low error rate of 0.37, as shown in table 5. The prevalence of this algorithm is emphasized by the large value of KS (0.97), indicating the classifier’s ground-breaking accuracy and performance. The performance of C4.5, featured by its minimal execution time and accuracy, puts it as a decent classifier. This characteristic makes it specifically well-suited for application in the healthcare sector, particularly for tasks involving prediction and classification. The application of data analytics methods for predictive analysis holds significant benefits in the health sector, as it gives us the power to predict and address potential threats to human health, covering different age groups, from the young ones to the elderly. This proactive method enables early disease detection, helping in timely interventions and contributing to better decision-making.

How do tangible and intangible rewards encourage employee voice? The perspective of dual proactive motivational pathways

Finding ways to motivate employees to speak up with suggestions and concerns is an important organizational concern. Drawing on the proactive motivation model, this research examines how organizational reward practices can encourage employee voice. We investigate whether tangible rewards (e.g. bonuses, pay increases, promotion) and intangible rewards (e.g. recognition, praise, positive feedback) can facilitate employee voice through the respective effects on employee psychological safety and organizational identification. Using three experiments across three different organizational and cultural contexts, we find consistent results supporting our hypotheses. Our three studies included working adults from the following regions: China (278 participants), United Kingdom (282 participants) and United States (272 participants). We conclude that employees make attributions about organizational reward practices whereby rewards signal to employees it is safe to voice and employees are valued. Our research demonstrates that both tangible and intangible rewards have indirect effects on employee voice by enhancing psychological safety, activating the ‘can do’ motivation process to voice, and by increasing their organizational identification that triggers the ‘reason to’ motivation process. We provide practical recommendations for organizations concerning how to use reward practices to best optimise psychological safety and organizational identification to encourage voice and suggest future research directions.

A proactive transshipment model for prototype parts logistics in the automotive industry

In the logistics of prototype parts in the automotive industry, unique parts are stored in warehouses and provided to assembly service providers to ensure a timely assembly of the associated prototype vehicles. As the allocation of the assembly orders to the assembly service providers may change before the start of the assembly, the shipments are planned at short notice. This article considers the short-term task of planning shipments of individual prototype parts from multiple warehouses to multiple assembly service providers. The current literature on planning shipments focuses mainly on parts available in batch sizes larger than one. However, as prototype parts are individual, storing them in adequate warehouses is crucial. A mixed-integer linear programming model for planning transshipments of parts between the warehouses and shipments to the assembly service providers to minimize overall costs is presented. The model enables the utilization of spare capacities of planned transports for a proactive transshipment of parts between warehouses. To this end, the future shipment costs of parts to assembly service providers are approximated to determine suitable warehouses. A numerical study shows that the proactive approach leads to a considerable cost reduction.

Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning.

The growth in the capabilities of telehealth have made it possible to identify individuals with a higher risk of uncontrolled diabetes and provide them with targeted support and resources to help them manage their condition. Thus, predictive modeling has emerged as a valuable tool for the advancement of diabetes management. This study aimed to conceptualize and develop a novel machine learning (ML) approach to proactively identify participants enrolled in a remote diabetes monitoring program (RDMP) who were at risk of uncontrolled diabetes at 12 months in the program. Registry data from the Livongo for Diabetes RDMP were used to design separate dynamic predictive ML models to predict participant outcomes at each monthly checkpoint of the participants' program journey (month-n models) from the first day of onboarding (month-0 model) up to the 11th month (month-11 model). A participant's program journey began upon onboarding into the RDMP and monitoring their own blood glucose (BG) levels through the RDMP-provided BG meter. Each participant passed through 12 predicative models through their first year enrolled in the RDMP. Four categories of participant attributes (ie, survey data, BG data, medication fills, and health signals) were used for feature construction. The models were trained using the light gradient boosting machine and underwent hyperparameter tuning. The performance of the models was evaluated using standard metrics, including precision, recall, specificity, the area under the curve, the F1-score, and accuracy. The ML models exhibited strong performance, accurately identifying observable at-risk participants, with recall ranging from 70% to 94% and precision from 40% to 88% across the 12-month program journey. Unobservable at-risk participants also showed promising performance, with recall ranging from 61% to 82% and precision from 42% to 61%. Overall, model performance improved as participants progressed through their program journey, demonstrating the importance of engagement data in predicting long-term clinical outcomes. This study explored the Livongo for Diabetes RDMP participants' temporal and static attributes, identification of diabetes management patterns and characteristics, and their relationship to predict diabetes management outcomes. Proactive targeting ML models accurately identified participants at risk of uncontrolled diabetes with a high level of precision that was generalizable through future years within the RDMP. The ability to identify participants who are at risk at various time points throughout the program journey allows for personalized interventions to improve outcomes. This approach offers significant advancements in the feasibility of large-scale implementation in remote monitoring programs and can help prevent uncontrolled glycemic levels and diabetes-related complications. Future research should include the impact of significant changes that can affect a participant's diabetes management.

Commentary] Healthspan Horizon — Pioneering Preventive Care as the New Standard of Healthcare

The contemporary healthcare landscape is experiencing a profound transformation, transitioning from a reactive to a proactive model focused on ongoing health maintenance, prediction, and disease prevention. This shift is driven by several factors, including the demographic shift towards an ageing population, increasing prevalence of chronic diseases, and economic challenges posed by the current hospital-centric care model. Proactive prevention, emphasizing early intervention and health promotion, emerges as imperative to mitigate the burden of chronic diseases and improve patient well-being. Innovative approaches leveraging technology, personalized medicine, and comprehensive risk management are at the forefront of this paradigm shift. This evolution requires significant investment and collaboration among stakeholders, however. Startups and investors will play a crucial role in driving meaningful innovation and navigating the regulatory landscape to realize the full potential of preventive medicine. Sustainability and efficiency in healthcare systems are paramount, necessitating smarter distribution of resources and prioritization of preventive care to ensure healthier populations and manageable expenditures. While implementation challenges exist, promising trends in personalized and digital health technologies offer new opportunities to address health proactively. Ultimately, the goal has to be to create a global healthcare ecosystem that prioritizes preventive measures and fosters interdisciplinary collaboration, ensuring access to quality healthcare for all. With this editorial, we want to initiate the discussion and subsequently provide more specific information through subsequent selected chapters on different clinical topics and future-oriented technological approaches.

Proactive Analysis and Detection of Cyber-attacks using Deep Learning Techniques

Objectives: This study objective is to create a proactive forensic framework with a classification model to identify the malicious content to avoid cyber-attacks. Methods: In this proposed work, a novel framework is introduced to analyze and detect network attacks before it happens. It monitors the network packet flow, captures the packets, analyzes the packet flow proactively, and detects cyber-attacks using different machine learning algorithms and Deep Convolution Neural network (CNN) technique. The KDD dataset is used in this experiment with 30% for testing and 80% for training. Findings: The simulation results show that the detection percentage of the proposed framework reaches a maximum of 95.92% in different scenarios. It is approximately 10% higher than the existing proactive frameworks for example Gawand’s model, Ahmetoglu’s model and many more. Novelty and applications: The proposed framework is a proactive model which detects the cyber-attack in prior to avoid cyber-attacks. The deep CNN model highly efficient for detecting cyber-attack. Keywords: Proactive Forensic Framework, Deep CNN, Classification Algorithms, Cyber attack detection, Intrusion Detection System

A Proactive Decision-Making Model for Evaluating the Reliability of Infrastructure Assets of a Railway System

Railway infrastructure is generally classified as either fixed or movable infrastructure assets. Failure in any of the assets could lead to the complete shutdown and disruption of the entire system, economic loss, inconvenience to passengers and the train operating company(s), and can sometimes result in death or injury in the event of the derailment of the rolling stock. Considering the importance of the railway infrastructure assets, it is only necessary to continuously explore their behavior, reliability, and safety. In this paper, a proactive multi-criteria decision-making model that is based on an interval-valued intuitionistic fuzzy set and some reliability quantitative parameters has been proposed for the evaluation of the reliability of the infrastructure assets. Results from the evaluation show that the failure mode ‘Broken and defective rails’ has the most risk and reliability concerns. Hence, priority should be given to the failure mode to avoid a total system collapse.