Inappropriate Resource Allocation Research Articles

Research-Driven Solutions for Enhancing Leadership in Competence-Based Curriculum Implementation in Kenyan Secondary Schools

Abstract: The study adopted a systematic review to analyze AI solutions in enhancing leadership in the implementation of CBC in junior secondary schools in Kenya. The study focuses on challenges relating to inadequate training of teachers, inappropriate resource allocation, and stakeholder engagement. We adopted Technology Acceptance Model (TAM) and Distributed Leadership Theory. The findings reveal that AI may enhance educational leadership by the implementation of real-time monitoring, preparation of teaching resources, and managing the institution more effectively. Moreover, the impeding factors for AI penetration in educational institutions are physical infrastructure, lack of digital literacy, and local conditions. In this regard, the study recommends partnerships between educational institutions and companies manufacturing technology equipment and digital training programs. Further research will focus on the development of specific AI applications to fit the Kenyan educational setup for serving all stakeholders: students, teachers, and school administrators.

Novelty-aware concept drift detection for neural networks

Neural network models are widely adopted in real-world applications for processing streaming data. However, these applications often face challenges in terms of accuracy degradation, caused by changes in the data distribution of the stream data compared to the training data. Two underlying reasons contribute to these changes. The first, known as the concept drift problem, occurs when there is a change in the correlation between the input data and the prediction output, making the models trained on the training data no longer suitable for the new data. The second reason, known as the novelty problem, arises when real-world data contains unexpected data categories that were not present in the training data, resulting in incorrect predictions. The research community has divided into different groups and each developed various methods to detect either concept drift or novelty distribution changes. However, these methods only address one aspect of the problem and are unable to distinguish between them. This leads to an inappropriate allocation of model maintenance resources, including the high cost of model retraining and the acquisition of true label data. In this study, we aim to address this gap by proposing a novel concept drift detection method that is capable of distinguishing between known labeled concept drift and novelty. Our method is also more efficient than existing drift detection methods, making it suitable for applications on neural networks.

NIMG-07. GLIOBLASTOMA AS A MIMIC OF ACUTE ISCHEMIC STROKE: AN UNDERRECOGNIZED PHENOMENON AND THE IMPACT ON PATIENT OUTCOMES

Abstract BACKGROUND Glioblastoma may present similarly to acute ischemic stroke. This study aims to determine characteristics and outcomes of patients with glioblastoma presenting with stroke-like symptoms. Our objective is to characterize patients with glioblastoma who present as mimics of acute ischemic stroke and identify differences in outcomes when compared to those diagnosed with glioblastoma only. METHODS The 2016-2019 National Inpatient Sample (NIS) database was queried for all patients who had a new diagnosis of glioblastoma. Patients with new glioblastoma who carried an admission diagnosis of acute ischemic stroke, but did not undergo thrombectomy, chemical thrombolysis, or hemicraniectomy were categorized as “GBM-Stroke Mimic”. The characteristics and comorbidities between GBM-Stroke Mimic and new glioblastoma patients were compared. Primary outcomes analyzed were discharge disposition and mortality. RESULTS Of 61,755 patients newly diagnosed with glioblastoma, 1,000 (1.6%) were GBM-Stroke Mimics. GBM-Stroke Mimic patients more commonly presented with aphasia (P < 0.001) and hemiplegia (P < 0.001). Age greater than 65, atrial fibrillation, diabetes mellitus, hypertension, hyperlipidemia, and long-term anticoagulation/antiplatelet therapy were more commonly associated with GBM-Stroke Mimic patients (all P < 0.001). GBM-Stroke Mimic patients were more likely to be discharged to skilled nursing facility, (p < 0.001), and a diagnosis of GBM-Stroke Mimic was an independent risk factor for inpatient death (P < 0.001). CONCLUSION To our knowledge, this is the first study formally investigating the clinical outcomes of glioblastoma when initially diagnosed as stroke. When this misdiagnosis is made, we found an association with increased inpatient mortality, delays of care, and inappropriate resource allocation.

Maladaptive behaviors in disasters: case study evaluation of Hurricane Hugo, Hurricane Katrina and the Haiti Earthquake.

Maladaptive behaviors during a disaster refer to actions that do not benefit the individual or society. Quarantelli highlights several maladaptive behaviors myths associated with disasters: widespread antisocial behavior, passivity, role conflict or abandonment, and sudden widespread mental health breakdowns (1). Despite early work reporting these myths, the common perception is that maladaptive behaviors such as rioting, looting, panic, and criminal conduct are prevalent in the wake of disasters. This is despite research by de Ville de Goyet and Arnold which has called on public officials and the media to stop propagating false disaster myths (2, 3). The classic academic response has been that this is a misconception and that, in fact, such behaviors are a very small part of the overall disaster and are mostly non-existent. Misconceptions about the prevalence of maladaptive behaviors can lead to inappropriate resource allocation, such as allocating extra police officers to prevent looting when the overall crime rate for the most part, decreases during disasters (4). Furthermore, while there are several persistent maladaptive behaviors myths, this is confounded by the presence of actual negative behaviors post disaster: false damage claims, insurance fraud, illegally obtaining relief supplies, failure to provide contracted repair services, hoarding of essential items, psychological trauma (which can lead to intergenerational transmission of the disaster memory) and medications and price gouging (5).When reading lay-press articles about recent disasters, it appears that these behaviors are on the rise. This raises the question: Has there been a change in the basic human reaction to disasters and are maladaptive behaviors on the rise? This review article focuses on case studies from three natural disasters: Hurricanes Hugo and Katrina, and the Haiti Earthquake. The goal of this review article is to evaluate these three natural disasters for evidence of maladaptive behaviors.

Patronage Pattern and Accessibility of Healthcare Facilities in Urban Slums and Informal Settlements in Ibadan Metropolis

Slums and informal settlements are residential areas physically and socially disintegrated. In spite of this, inadequate health-related data on slum areas and informal settlements has led to inappropriate and unrealistic allocation of healthcare resources by the public and private health providers. This study investigates the patronage pattern and accessibility of healthcare facilities to residents of urban slums and Informal settlements in Ibadan Metropolis, a rapidly growing urban area in Nigeria facing challenges related to urbanization and healthcare infrastructure. Data were collected from both primary and secondary sources. Structured questionnaire was administered on 1,389 residents of the slummy neighbourhoods and informal settlements in Ibadan metropolis, and obtained data were analyzed using descriptive and inferential statistics. Results revealed that out of 109 neighbourhoods, 42.1% were categorized as slummy neighbourhoods, 36.7% as slums/informal settlements, while 21.2% were informal settlement using slummy and informality conditions respectively. Results revealed that distance to health facility (β= -0.353, p<0.05), educational qualification (β=-0.063, p<0.05) and household size (β= -0.052, p<0.05) had an inverse relationship with patronage of health facilities, while age (β=0.086, p< 0.05) had a positive relationship. It was also revealed that access to PHC and dispensary/clinic in slummy neighbouhoods and slum/informal settlements is higher compared to informal settlements in the study area. The study therefore recommends implementation of slum improvement program for the slummy and slums/informal neighbourhoods, provision of additional healthcare facilities to be located especially in the slummy neighbourhoods and informal settlements to ensure equitable distribution.

Durasi Observasi Ideal untuk Penentuan Kebutuhan Tenaga Non Kesehatan Rumah Sakit dengan Metode WISN: Tinjauan Literatur

The increasing need for trained healthcare workers is currently faced by most hospitals in developing countries. Hospitals as health service providers need to plan policies that ensure the availability and sufficiency of human resources. Inappropriate proportions and allocation of human resources can have a negative impact on service quality. The WISN methodology helps determine the number of health workers of a particular type needed to cover the workload. However, in the guidelines, there is no ideal limit for the duration of observations carried out by institutions to obtain accurate and representative data. The purpose of this article is to determine the duration of observation to determine the need for non-health hospital personnel using the WISN method. The method used in this literature review is library research sourced from online databases such as Mendeley and Google Scholar. This study focuses on analyzing the workload of non-health human resources within hospitals and the ideal duration of observation in research by searching using keywords with boolean approach. We found 92 articles and after evaluation, five articles were selected for this study. From various studies, we found that there is still an excessive workload in several institutions so it could be a consideration to increase staff number according to the WISN method. According to the duration of observation, in research with a duration of ten days, the description of activities that can be recorded cannot represent the entire activity and some problems cannot be completely identified due to time limitations.

A Coordinated Allocation Method for Right-Turn Strategy at Signalized Intersections with Optimal Pedestrian and Vehicle Delays

With strict enforcement of pedestrian right of way at all intersections, the inappropriate right-turn resource allocation from a spatial and temporal perspective will lead to a reduction in the operational efficiency of the intersection. In this paper, three spatiotemporal resource allocation schemes for right-turning vehicles are proposed, considering the vehicle and pedestrian traffic efficiency in all directions of the intersection. To minimize vehicle and pedestrian delay at the intersection individually, an optimization model is established with the effective green time of each phase and three schemes as decision variables. A right-turn vehicle and pedestrian conflict delay model is developed based on the pedestrian-vehicle interaction behavior as the constraints of the optimization model. The NSGA-II algorithm is used to solve the model, and the quantitative criteria for the exclusive right-turn lane and phase are obtained by sensitivity analysis. The results of this paper can be used as a guide for traffic design and for planning and controlling the operation of right-turning vehicles at intersections.

Assessing the risk of endogeneity bias in health and mortality inequalities research using composite measures of multiple deprivation which include health-related indicators: A case study using the Scottish Index of Multiple Deprivation and population health and mortality data.

The inclusion of health-related indicators in composite measures of multiple deprivation introduces a risk of endogeneity bias when using the latter in health inequalities research. This bias may ultimately result in the inappropriate allocation of healthcare resources and maintenance of preventable health inequalities. Mitigation strategies to avoid this bias include removing the health-related indicators or using single constituent domains (such as income or employment class) in isolation. These strategies have not been widely validated. This study used population-level health and mortality data with a contemporary composite measure of multiple deprivation (Scottish Index of Multiple Deprivation; SIMD) to assess these mitigation strategies. The differences between deprivation methods (original, health excluded, and income domain) were negligible. The results of quantitative research on health inequalities are unlikely to be affected by endogeneity bias.

Optimizing protection resource allocation for traffic-driven epidemic spreading.

Optimizing the allocation of protection resources to control the spreading process in networks is a central problem in public health and network security. In this paper, we propose a comprehensive adjustable resource allocation mechanism in which the over allocation of resources can be also numerically reflected and study the effects of this mechanism on traffic-driven epidemic spreading. We observe that an inappropriate resource allocation scheme can induce epidemic spreading, while an optimized heterogeneous resource allocation scheme can significantly suppress the outbreak of the epidemic. The phenomenon can be explained by the role of nodes induced by the heterogeneous network structure and traffic flow distribution. Theoretical analysis also gives an exact solution to the epidemic threshold and reveals the optimal allocation scheme. Compared to the uniform allocation scheme, the increase in traffic flow will aggravate the decline of the epidemic threshold for the heterogeneous resource allocation scheme. This indicates that the uneven resource allocation makes the network performance of suppressing epidemic degrade with the traffic load level. Finally, it is demonstrated that real-world network topology also confirms the results.

Triage Errors in Primary and Pre-Primary Care.

Triage errors are a major concern in health care due to resulting harmful delays in treatments or inappropriate allocation of resources. With the increasing popularity of digital symptom checkers in pre–primary care settings, and amid claims that artificial intelligence outperforms doctors, the accuracy of triage by digital symptom checkers is ever more scrutinized. This paper examines the context and challenges of triage in primary care, pre–primary care, and emergency care, as well as reviews existing evidence on the prevalence of triage errors in all three settings. Implications for development, research, and practice are highlighted, and recommendations are made on how digital symptom checkers should be best positioned.

Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds.

Containers have emerged as a more portable and efficient solution than virtual machines for cloud infrastructure providing both a flexible way to build and deploy applications. The quality of service, security, performance, energy consumption, among others, are essential aspects of their deployment, management, and orchestration. Inappropriate resource allocation can lead to resource contention, entailing reduced performance, poor energy efficiency, and other potentially damaging effects. In this paper, we present a set of online job allocation strategies to optimize quality of service, energy savings, and completion time, considering contention for shared on-chip resources. We consider the job allocation as the multilevel dynamic bin-packing problem that provides a lightweight runtime solution that minimizes contention and energy consumption while maximizing utilization. The proposed strategies are based on two and three levels of scheduling policies with container selection, capacity distribution, and contention-aware allocation. The energy model considers joint execution of applications of different types on shared resources generalized by the job concentration paradigm. We provide an experimental analysis of eighty-six scheduling heuristics with scientific workloads of memory and CPU-intensive jobs. The proposed techniques outperform classical solutions in terms of quality of service, energy savings, and completion time by 21.73–43.44%, 44.06–92.11%, and 16.38–24.17%, respectively, leading to a cost-efficient resource allocation for cloud infrastructures.

A deterministic river temperature model to prioritize management of riparian woodlands to reduce summer maximum river temperatures

AbstractIncreasing river temperatures are a threat to cold water species including ecologically and economically important freshwater fish, such as Atlantic salmon. In 2018, ca. 70% of Scottish rivers experienced temperatures which cause thermal stress in juvenile salmon, a situation expected to become increasingly common under climate change. Management of riparian woodlands is proven to protect cold water habitats. However, creation of new riparian woodlands can be costly and logistically challenging. It is therefore important that planting can be prioritized to areas where it is most needed and can be most effective in reducing river temperatures. The effects of riparian woodland on channel shading depend on complex interactions between channel width, orientation, aspect, gradient, tree height and solar geometry. Subsequent effects on river temperature are influenced by water volume and residence time. This study developed a deterministic river temperature model, driven by energy gains from solar radiation that are modified by water volume and residence time. The resulting output is a planting prioritization metric that compares potential warming between scenarios with and without riparian woodland. The prioritization metric has a reach scale spatial resolution, but can be mapped at large spatial scales using information obtained from a digital river network. The results indicate that water volume and residence time, as represented by river order, are a dominant control on the effectiveness of riparian woodland in reducing river temperature. Ignoring these effects could result in a sub‐optimal prioritization process and inappropriate resource allocation. Within river order, effectiveness of riparian shading depends on interactions between channel and landscape characteristics. Given the complexity and interacting nature of controls, the use of simple universal planting criteria is not appropriate. Instead, managers should be provided with maps that translate complex models into readily useable tools to prioritize riparian tree planting to mitigate the impacts of high river temperatures.

Interpretive structural modelling of inter-agency collaboration risk in public safety networks

Inter-agency collaboration is a well-established, yet very difficult process in public governance. Despite the fact that it is often unsuccessful, collaboration risk research is still undeveloped and the impact of this risk on the effectiveness of joint activities is still underestimated. This issue is of particular significance in public safety networks, where inter-agency collaboration processes are conducted under the conditions of the complexity and uncertainty. For this reason, the article is intended to: (1) identify factors of collaboration risk in public safety networks, (2) determine the impact of individual risk factors on inter-agency collaboration outcomes, (3) identify the relationship between risk factors of inter-agency collaboration in public safety networks, and (4) analyse the growth of this risk in public safety networks. These purposes were achieved using the Systematic Literature Review based on PRISMA Group methodology and Interpretive Structural Modelling together with MICMAC analysis. The applied research approach also has some limitations resulting from the number of experts. However, the results obtained allow us to better understand issues of inter-agency collaboration risk in public safety networks. They identify key collaboration risk factors, such as inappropriate collaboration rules and inadequate allocation of tasks and resources. In consequence, they indicate risk symptoms that are worth keeping track of in order to prevent collaboration ineffectiveness.

Intraoperative waste segregation initiative among anesthesia personnel to contain disposal costs

Approximately 90% of medical waste generated in the operating room (OR) is considered to be non-infectious and non-regulated (Wyssusek, Keys & van Zundert, 2019). Frequently, this waste is inappropriately disposed of into infectious regulated medical waste containers. Due to differences in waste treatment, improper segregation can lead to the misuse or inappropriate allocation of resources, environmental pollution, and increased cost for institutions. A waste segregation initiative was instituted in a tertiary care medical center in the anesthesia work-space of 35 ORs. This initiative included education of medical waste management to increase anesthesia staff knowledge and compliance with waste segregation and optimization of existing waste disposal containers to decrease waste disposal costs. After implementation, there was an increase in overall provider knowledge (p < 0.001) particularly in the categories of medication vial disposal, medication disposal and identification of items for disposal in the sharps containers (p ≤ 0.05). Data suggests a 34.7% increase in providers reporting to always practice waste segregation (p ≤ 0.05). Additionally, there was a statistically significant decrease in overall weight of regulated medical waste items from 0.33 kg/case to 0.09 kg/case (p < 0.001). This decrease in regulated waste supports an improvement in waste segregation and inappropriate items being disposed of in the general trash container. The omission of inappropriate waste was further confirmed by a segregation audit that showed an overall increase in correctly segregated regulated waste of 65%. Collectively, this lead to a cost savings of $15.60 per OR per week, or $28,392 annually.

Prevalence and effects of suicidal ideation diagnosis code position in claims on readmission rate estimates.

Suicidal ideation (SI) is a major health concern in children, adolescents, and young adults (CAYA) population. Inaccurate estimates of SI-related hospital readmission rates may contribute to inappropriate allocation of resources for the prevention of future readmissions. The estimation of these readmission rates using claims data may be sensitive to the diagnosis code position used to establish analytic cohorts. To examine the prevalence and effects of SI diagnosis code position in claims on 30-day readmission rates using the Nationwide Readmissions Database (NRD). This was a cross-sectional study using the NRD (2010-2015). We established six cohorts of hospitalized CAYA (5-24 years old) with a diagnosis of SI based on different combinations of SI diagnosis code (ICD-9 code V62.84) positions in claims. Thirty-day hospital readmission rates following an index SI discharge were estimated for each cohort. We tested the null hypothesis that hospital readmission rates following an index SI discharge are not sensitive to diagnosis code positions using a test for equality of proportions between the predefined SI cohorts. The prevalence of SI diagnosis codes increased yearly from 2.9% in 2010 to 5.8% in 2015. SI hospital readmission rates ranged from 0 to 41.1 per 1000 index events based on cohort definitions (i.e. diagnosis code positions). We rejected the null hypothesis that SI-related readmission rates are not sensitive to diagnosis code positions. SI-related readmission rate estimates are sensitive to SI diagnosis code positions. Determining appropriate diagnostic positions can further improve readmission analyses for SI and its applications in healthcare policies.

ATA2020 Session Summaries (Abstracts)

ATA2020 Session Summaries (Abstracts)

Social media for improved process management in organizations during disasters

Natural and man‐made disasters potentially cause significant damage and disruption to communities. As population density increases and natural disasters become more extreme, it becomes increasingly important to both manage communications and extract information from communications to be able to mitigate the negative effects of such disasters. The emergence of social media platforms has led to new avenues for the collection and dissemination of information that is either local or global. Although social studies have revealed that social media feeds can improve effective disaster preparedness and recovery, it has been observed that the use of social media can have severe negative consequences through the rapid spread of false information leading to the inappropriate allocation of resources and in extreme cases panic and lawlessness. Rumours and false information are likely to affect appropriate corporate responses as well as, where appropriate, responses of public organizations tasked with appropriately responding to natural and man‐made disasters. The ability to identify instances of false information through the course of natural and man‐made disasters is a critical capability for corporate and public bodies in order to improve disaster management and response.To reduce the impact of these rumours, a technique is proposed that makes use of supervised learning to differentiate between information about an actual event from information about a false one and communicating it effectively to appropriate organizations. For this purpose, 934 social media feeds were analysed using a Naïve Bayes classifier. Clearly, applied early on this technique potentially can improve the quality of disaster response and recovery and mitigate the negative consequences.Broadly speaking, we consider this research to relate to the management of knowledge and information flow in disaster situations. Clearly, the techniques introduced in this paper, in providing individuals and organizations with access to knowledge rather than false information and rumours, will help organizations manage resources and activities during disasters more efficiently and effectively. The study concludes with the implications, limitations, and future directions.

The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example.

Eliminating unnecessary healthcare waste in hospitals and providing better healthcare quality are the core issues of green supply chain management (GSCM). Hence, this study used a hospital’s emergency department crowding (EDC) problem to illustrate how to establish an emergency medicine service (EMS) simulation system to obtain a robust parameters setting for solving hospitals’ EDC and waste problems, thereby increasing healthcare quality. Inappropriate resource allocation results in more serious EDC; more serious EDC results in increasing operating costs. Therefore, in the healthcare system, waste includes inappropriate costs and inappropriate resource allocation. The EMS of a medical center in central Taiwan was the object of the study. In this study, the dynamic Taguchi method was used to set the signal factor, noise factor, and control factors to simulate the EMS system to obtain the optimal parameters setting. The performance was set to Emergency Department Work Index (EDWINC) and system time (waiting time and service time) per patient. The signal factor was set to the number of physicians; the noise factor was set to patient arrival rate; the control factors included persuading Triage 4 and Triage 5 outpatients, checkup process, bed occupation rate in the emergency department (ED), and medical checkup sequence for Triage 4 and Triage 5 patients. This study makes two significant contributions. First, the study introduces the GSCM concept to the healthcare setting to bring green innovation to hospitals. Hospital administrators may hence design better GSCM activities to facilitate healthcare processes to provide better healthcare outcomes. Second, the study applied the dynamic Taguchi method to the EMS and neural network (NN) to construct a computational model revealing the cause (factors) and effect (performances) relationship. In addition, the genetic algorithm (GA), a solution method, was used to obtain the optimal parameters setting of the EDC in Taiwan. Hence, after obtaining the solutions, the unnecessary waste in EDC—inappropriate costs and inappropriate resource allocation—is reduced.

Stakes in Testing: Not a Simple Dichotomy but a Profile of Consequences That Guides Needed Evidence of Measurement Quality

Testing programs are often classified as high or low stakes to indicate how stringently they need to be evaluated. However, in practice, this classification falls short. A high‐stakes label is taken to imply that all indicators of measurement quality must meet high standards; whereas a low‐stakes label is taken to imply the opposite. This approach can result in inappropriate allocation of resources and inadequate attention to needed evidence. We argue that “stakes” are better thought of as a profile of consequences. We suggest generalizable criteria for evaluating and responding to stakes in testing, with applications to licensure, employment, and K–12 accountability testing.

A new approach for resource allocation for black spot treatment (case study: The road network of Iran)

Introduction: Currently, spatial and temporal distribution of safety resources in Iran is entirely based on expert opinions, regardless of network priorities. Considering the lack of resources for implementing safety treatments, prioritizing unsafe points is an important and complicated issue where the effectiveness of each safety treatment option should be thoroughly investigated. The political, social, and environmental aspects should also be taken into consideration, including social and political pressures and officials talks on less important topics. Obviously, this inappropriate resource allocation poses a serious challenge to the expected goals. In this study, a methodology based on economic and social issues is proposed to optimize the annual budget allocation for eliminating or reducing the risk of accident-prone points. In this methodology, the spatial and temporal distribution of budget is determined using a mathematical model aimed to maximize the benefits of reducing the accidents after deducting the costs of implementing the safety countermeasures. The outputs of this model include the safety countermeasure alternatives and a five-year time schedule for implementing them, or the alternative of no action with regard to budget, social, and judicial constraints. In order to evaluate the proposed method, it is applied to the road network of Iran and the results are compared with those of the conventional method that is currently used for resource allocation in this country. The results show that the proposed method leads to 15% higher benefits compared to the conventional method. Moreover, this method makes 641 safe points, which is about 17% more than the safe points resulted from the existing method. Therefore, the proposed method brings about a safer network as a result of the optimal allocation of available resources.