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South African Medical Journal =... Dec 2023No matter which benefit option members have chosen, medical schemes are required by the Medical Schemes Act no. 131 of 1998 to pay costs associated with the diagnosis,...
BACKGROUND
No matter which benefit option members have chosen, medical schemes are required by the Medical Schemes Act no. 131 of 1998 to pay costs associated with the diagnosis, treatment, or care of a specified set of benefits known as Prescribed Minimum Benefits (PMBs). Medical scheme beneficiaries have the right to lodge complaints with the Council for Medical Schemes (CMS) when their claims are denied.
OBJECTIVE
To determine and describe the pattern of PMBs complaints received by CMS from January 2014 to December 2018.
METHODS
This was a cross-sectional study that utilised the CMS' clinical complaints. Data for PMBs, complainants, medical scheme types, and reasons for payment denial were extracted. The CMS' lists of chronic conditions, PMBs, and registered schemes were used to confirm PMBs and to categorise schemes as either restricted (i.e., to only members of specific organisations) or open (i.e., to all South Africans). Extracted and coded data were analysed using SAS v.9.4 software.
RESULTS
A total of 2141 complaints were retrieved and 1124 PMBs complaints were included in the study. The median of PMBs complaints per year was 225. Most of the complaints (43.6%, n=490/1124) were lodged by members themselves. Non-Communicable Diseases (NCDs) constituted most of the PMBs conditions that members complained about. Medicine and surgery were the services that were mostly denied full payment by medical schemes. Open medical schemes accounted for more (73.8%, n=830/1124) of the complaints.
CONCLUSION
Chronic conditions are the main diseases that medical scheme members complained about. Member education and clear definition of PMBs should be prioritised by medical schemes and the Council for Medical Schemes.
Topics: Humans; Retrospective Studies; Cross-Sectional Studies; South Africa; Costs and Cost Analysis; Chronic Disease
PubMed: 38525635
DOI: No ID Found -
Journal of Bioethical Inquiry Mar 2024The conflict in Gaza and Israel that ignited on October 7, 2023 signals a catastrophic breakdown in the possibility of ethical dialogue in the region. The actions on...
The conflict in Gaza and Israel that ignited on October 7, 2023 signals a catastrophic breakdown in the possibility of ethical dialogue in the region. The actions on both sides have revealed a dissolution of ethical restraints, with unimaginably cruel attacks on civilians, murder of children, destruction of health facilities, and denial of basic needs such as water, food, and shelter. There is a need both to understand the nature of the ethical singularity represented by this conflict and what, if any, options are available to allow the reconstruction of communication between the warring parties. This article seeks to address these tasks by analysing the conflict as inherently an ethical one, in the sense that it exposes a rupture in the fabric of communicative relationships that has evolved systematically out of the deep cultural structures from which all protagonists have emerged. Drawing on the work of Levinas, Habermas, Arendt, and others, and referring to the specific circumstances in the region, it examines the ethical sources of the crisis and tries to identify conditions for its resolution. The possibility of reconciliation-that is, of refiguring relationships to open up a space for dialogue to create pathways to heal the ruptures-is examined. The dark legacy of the Holocaust is identified as an abiding cultural vulnerability for both societies. It is concluded, however, that the rich history of partnerships and collaborations between Jews and Palestinians provides a robust infrastructure on the basis of which a sustainable peace might be built, providing a much-needed source of hope.
Topics: Humans; Arabs; Communication; Israel; Middle East; Negotiating; Armed Conflicts; Middle Eastern People
PubMed: 38517636
DOI: 10.1007/s11673-024-10347-x -
Dialogues in Health Jun 2024The aim of this study was to analyze the well-being and coping strategies of nurses working in an organizational setting perceived as characterized by workplace...
AIM
The aim of this study was to analyze the well-being and coping strategies of nurses working in an organizational setting perceived as characterized by workplace bullying. The innovative aspect of this study is that we considered only those who perceive to work in an organizational environment characterized by workplace bullying, and not those who see themselves as victims and those who perceive they work in an organizational environment not characterized by workplace bullying.
METHOD
A questionnaire with the NAQ-R, PGWBI, Val.Mob. and Brief COPE scales was administered to nurses. To better understand this phenomenon, a comparison was made between 331 nurses and 166 workers in other professions who also work in an organizational environment perceived to be characterized by workplace bullying.
RESULTS
In both groups (nurses and workers), the results were approximately the same in terms of personal bullying and workplace bullying episodes and the number of physical and emotive symptoms. The PGWBI score was lower for nurses than for workers in other fields. Among the individual symptoms, nurses and registered nurses were more likely to report gastritis, insomnia and heartburn than workers in other contexts. Workers in other contexts were more likely than nurses to report symptoms of anxiety, fear, feelings of insecurity, inferiority and guilt. In terms of coping strategies, nurses were more likely than other workers to report distraction, substance use, emotional support, disengagement, venting, positive reframing, humor, and religion. Workers in other professional context were more likely than nurses to report active coping, denial, instrumental support, planning, acceptance, and self-blame.
CONCLUSION
Results suggest that the consequences of working in a perceived organizational environment characterized by workplace bullying are similar for both groups of workers, with nonstatistical differences in perceived workplace bullying episodes and sum of physical and emotive symptoms.
IMPLICATION
Overall, findings suggest that workplace bullying prevention is a fundamental element in training workers in all types of workplaces and should be an integral part of curriculum activities.
PubMed: 38516220
DOI: 10.1016/j.dialog.2024.100174 -
Revista Brasileira de Enfermagem 2024to report an educational technology construction on nursing professionals' rights.
OBJECTIVES
to report an educational technology construction on nursing professionals' rights.
METHODS
an experience report on educational technology construction during the crediting of university extension hours in an undergraduate nursing course at a Brazilian public university, between March and June 2023. The Deming cycle was used as a procedural method.
RESULTS
four meetings were held between students and extension workers. Eight comic books were produced based on the Code of Ethics for Nurses, addressing professional autonomy, fair remuneration, risk-free work, denial of exposure in the media and others. The Deming cycle proved to be an important strategy for constructing products.
CONCLUSIONS
nursing professionals' rights must be discussed and improved. Educational technologies, such as comic books, provide playful and reflective learning.
Topics: Humans; Education, Nursing, Baccalaureate; Inventions; Students, Nursing; Educational Status; Learning
PubMed: 38511827
DOI: 10.1590/0034-7167-2023-0438 -
Harm Reduction Journal Mar 2024HIV prevalence among people who use drugs (PWUD) in Tanzania is 4-7 times higher than in the general population, underscoring an urgent need to increase HIV testing and...
Adapting a health facility HIV stigma-reduction participatory training intervention to address drug use stigma in HIV care and treatment clinics in Dar es Salaam, Tanzania.
BACKGROUND
HIV prevalence among people who use drugs (PWUD) in Tanzania is 4-7 times higher than in the general population, underscoring an urgent need to increase HIV testing and treatment among PWUD. Drug use stigma within HIV clinics is a barrier to HIV treatment for PWUD, yet few interventions to address HIV-clinic drug use stigma exist. Guided by the ADAPT-ITT model, we adapted the participatory training curriculum of the evidence-based Health Policy Plus Total Facility Approach to HIV stigma reduction, to address drug use stigma in HIV care and treatment clinics (CTCs).
METHODS
The first step in the training curriculum adaptation process was formative research. We conducted 32 in-depth interviews in Dar es Salaam, Tanzania: 18 (11 men and 7 women) with PWUD living with HIV, and 14 with a mix of clinical [7] and non-clinical [7] CTC staff (5 men and 9 women). Data were analyzed through rapid qualitative analysis to inform initial curriculum adaptation. This initial draft curriculum was then further adapted and refined through multiple iterative steps of review, feedback and revision including a 2-day stakeholder workshop and external expert review.
RESULTS
Four CTC drug use stigma drivers emerged as key to address in the curriculum adaptation: (1) Lack of awareness of the manifestations and consequences of drug use stigma in CTCs (e.g., name calling, ignoring PWUD and denial of care); (2) Negative stereotypes (e.g., all PWUD are thieves, dangerous); (3) Fear of providing services to PWUD, and; (4) Lack of knowledge about drug use as a medical condition and absence of skills to care for PWUD. Five, 2.5-hour participatory training sessions were developed with topics focused on creating awareness of stigma and its consequences, understanding and addressing stereotypes and fears of interacting with PWUD; understanding drug use, addiction, and co-occurring conditions; deepening understanding of drug use stigma and creating empathy, including a panel session with people who had used drugs; and working to create actionable change.
CONCLUSION
Understanding context specific drivers and manifestations of drug use stigma from the perspective of PWUD and health workers allowed for ready adaptation of an existing evidence-based HIV-stigma reduction intervention to address drug use stigma in HIV care and treatment clinics. Future steps include a pilot test of the adapted intervention.
Topics: Male; Humans; Female; Tanzania; Social Stigma; Substance-Related Disorders; HIV Infections; Health Facilities
PubMed: 38491349
DOI: 10.1186/s12954-024-00965-4 -
Psychiatry, Psychology, and Law : An... 2024The study employed inductive-thematic analysis to identify dynamic cognitive-emotional processes occurring in proximity to deliberate firesetting among a sample of...
The study employed inductive-thematic analysis to identify dynamic cognitive-emotional processes occurring in proximity to deliberate firesetting among a sample of = 35 adjudicated juvenile firesetters. Six fire-specific themes were determined. Three of these themes are akin to an implicit theory (i.e. a belief system informed by previous experiences): Fire Interest, Fire is Controllable, and Fire Denial/Accidental. Three of these themes are consistent with a cognitive script (i.e. a behavioural guide for how and when to use fire): (a) fire is destructive; (b) fire conceals evidence; and (c) fire creates calm. When reviewed more closely, the theme 'fire is destructive' is composed of two separate subcategories: 'fire creates destructive fun' and 'fire is a destructive tool for revenge'. The findings have risk assessment and treatment implications for juvenile firesetters.
PubMed: 38476296
DOI: 10.1080/13218719.2023.2175067 -
Sensors (Basel, Switzerland) Mar 2024The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed...
The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) attacks emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantity and prior detection of DDoS attacks has seen a decline in recent methods. This research introduces an innovative approach by integrating evolutionary optimization algorithms and machine learning techniques. Specifically, the study proposes XGB-GA Optimization, RF-GA Optimization, and SVM-GA Optimization methods, employing Evolutionary Algorithms (EAs) Optimization with Tree-based Pipelines Optimization Tool (TPOT)-Genetic Programming. Datasets pertaining to DDoS attacks were utilized to train machine learning models based on XGB, RF, and SVM algorithms, and 10-fold cross-validation was employed. The models were further optimized using EAs, achieving remarkable accuracy scores: 99.99% with the XGB-GA method, 99.50% with RF-GA, and 99.99% with SVM-GA. Furthermore, the study employed TPOT to identify the optimal algorithm for constructing a machine learning model, with the genetic algorithm pinpointing XGB-GA as the most effective choice. This research significantly advances the field of DDoS attack detection by presenting a robust and accurate methodology, thereby enhancing the cybersecurity landscape and fortifying digital infrastructures against these pervasive threats.
PubMed: 38475208
DOI: 10.3390/s24051672 -
Sensors (Basel, Switzerland) Mar 2024The Message Queuing Telemetry Transport (MQTT) protocol stands out as one of the foremost and widely recognized messaging protocols in the field. It is often used to...
The Message Queuing Telemetry Transport (MQTT) protocol stands out as one of the foremost and widely recognized messaging protocols in the field. It is often used to transfer and manage data between devices and is extensively employed for applications ranging from smart homes and industrial automation to healthcare and transportation systems. However, it lacks built-in security features, thereby making it vulnerable to many types of attacks such as man-in-the-middle (MitM), buffer overflow, pre-shared key, brute force authentication, malformed data, distributed denial-of-service (DDoS) attacks, and MQTT publish flood attacks. Traditional methods for detecting MQTT attacks, such as deep neural networks (DNNs), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and fuzzy logic, may exist. The increasing prevalence of device connectivity, sensor usage, and environmental scalability become the most challenging aspects that novel detection approaches need to address. This paper presents a new solution that leverages an H2O-based distributed machine learning (ML) framework to improve the security of the MQTT protocol in networks, particularly in IoT environments. The proposed approach leverages the strengths of the H2O algorithm and architecture to enable real-time monitoring and distributed detection and classification of anomalous behavior (deviations from expected activity patterns). By harnessing H2O's algorithms, the identification and timely mitigation of potential security threats are achieved. Various H2O algorithms, including random forests, generalized linear models (GLMs), gradient boosting machine (GBM), XGBoost, and the deep learning (DL) algorithm, have been assessed to determine the most reliable algorithm in terms of detection performance. This study encompasses the development of the proposed algorithm, including implementation details and evaluation results. To assess the proposed model, various evaluation metrics such as mean squared error (MSE), root-mean-square error (RMSE), mean per class error (MCE), and log loss are employed. The results obtained indicate that the H2OXGBoost algorithm outperforms other H2O models in terms of accuracy. This research contributes to the advancement of secure IoT networks and offers a practical approach to enhancing the security of MQTT communication channels through distributed detection and classification techniques.
PubMed: 38475174
DOI: 10.3390/s24051638 -
Sensors (Basel, Switzerland) Feb 2024Cloud computing has revolutionized the information technology landscape, offering businesses the flexibility to adapt to diverse business models without the need for...
Cloud computing has revolutionized the information technology landscape, offering businesses the flexibility to adapt to diverse business models without the need for costly on-site servers and network infrastructure. A recent survey reveals that 95% of enterprises have already embraced cloud technology, with 79% of their workloads migrating to cloud environments. However, the deployment of cloud technology introduces significant cybersecurity risks, including network security vulnerabilities, data access control challenges, and the ever-looming threat of cyber-attacks such as Distributed Denial of Service (DDoS) attacks, which pose substantial risks to both cloud and network security. While Intrusion Detection Systems (IDS) have traditionally been employed for DDoS attack detection, prior studies have been constrained by various limitations. In response to these challenges, we present an innovative machine learning approach for DDoS cloud detection, known as the Bayesian-based Convolutional Neural Network (BaysCNN) model. Leveraging the CICDDoS2019 dataset, which encompasses 88 features, we employ Principal Component Analysis (PCA) for dimensionality reduction. Our BaysCNN model comprises 19 layers of analysis, forming the basis for training and validation. Our experimental findings conclusively demonstrate that the BaysCNN model significantly enhances the accuracy of DDoS cloud detection, achieving an impressive average accuracy rate of 99.66% across 13 multi-class attacks. To further elevate the model's performance, we introduce the Data Fusion BaysFusCNN approach, encompassing 27 layers. By leveraging Bayesian methods to estimate uncertainties and integrating features from multiple sources, this approach attains an even higher average accuracy of 99.79% across the same 13 multi-class attacks. Our proposed methodology not only offers valuable insights for the development of robust machine learning-based intrusion detection systems but also enhances the reliability and scalability of IDS in cloud computing environments. This empowers organizations to proactively mitigate security risks and fortify their defenses against malicious cyber-attacks.
PubMed: 38474952
DOI: 10.3390/s24051418 -
JAMA Health Forum Mar 2024
Topics: Humans; Warfare; Ethnicity; Health Facilities; Delivery of Health Care; Russia
PubMed: 38457128
DOI: 10.1001/jamahealthforum.2024.0031