Priority Information Research Articles

Information and communication priorities of patients and healthcare professionals in shared decision making regarding adjuvant systemic breast cancer treatment: A survey study

PurposeTo assess information and communication priorities of patients and healthcare professionals in Shared Decision Making about adjuvant systemic treatment of primary breast cancer and identify key decision-relevant information accordingly. MethodsPatients (N = 122) and professionals working with breast cancer patients (N = 118), of whom 38 were nurse practitioners and 32 nurses, were recruited using convenience sampling, and surveyed about information/communication aspects key to decision-making, using ranking assignments. We further posed a simple open question, questions about receiving population-based statistics versus personalized statistics concerning treatment outcomes, and their attitude and experience concerning Shared Decision Making. Data were analyzed using descriptive analysis and a qualitative analysis. ResultsBoth patients and professionals prioritized information about treatment outcomes (i.e., survival, recurrence) as key decision-relevant information for patients. Patients prioritized information about relatively severe treatment side-effects and late effects (e.g., blood clot, stroke), whilst professionals prioritized information about effects that occur relatively often (e.g., hair loss, fatigue). Patients specifically wanted to know if the benefit of treatment is worth the negative impact. Both groups prioritized personalized statistics over population-based statistics. ConclusionsSome differences between patients and professionals were found in information and communication priorities, specifically related to the different side-effects. It seems worthwhile to precisely address these side-effects in Shared Decision Making concerning adjuvant systemic treatment. Furthermore, it seems important to deliberate together on the question if expected benefit of treatment is worth the potential negative impact for the individual patient.

Digital society: State and development prospects

The digital society is characterised by the use of various technologies and exhibits diverse developmental trends. Digitalisation facilitates everyday life and enhances business process efficiency. The research aims to identify the origins of the digital society, assess its current state of development and future prospects, and analyse the impact of digitalisation on social life. The methodology is based on scientific research principles, employing general scientific methods of cognition, critical analysis, observation, and synthesis. The article reveals the role and significance of artificial intelligence in the development of the digital society and the potential problems and risks of its use: the generation and dissemination of misinformation, risks of violating personal privacy, risks of increasing inequality, and the emergence of ethical dilemmas in the use of artificial intelligence. It is concluded that alongside the opportunities opened up by the digital society, there are challenges and threats. These include overcoming the information crisis, recognising the priority of information compared to other resources, and forming a new sector of the information economy. Alongside these benefits, challenges arise, such as the impact of digital communication channels, the intrusion of technology into private life, and issues concerning the reliability of information. Ensuring cybersecurity and privacy is becoming a crucial and pressing task. It is also necessary to ensure responsible use of technology and artificial intelligence to avoid negative consequences such as inequality in access to technology and job losses due to digital transformation. While there is great potential for using technology to improve lives, its development and implementation must be carefully managed to maximise benefits and minimise risks. This will require global cooperation, responsible policies, ethical approaches, and the preparation of citizens for the realities of the digital world

Consensus reporting guidelines to address gaps in descriptions of ultra-rare genetic conditions

Genome-wide sequencing and genetic matchmaker services are propelling a new era of genotype-driven ascertainment of novel genetic conditions. The degree to which reported phenotype data in discovery-focused studies address informational priorities for clinicians and families is unclear. We identified reports published from 2017 to 2021 in 10 genetics journals of novel Mendelian disorders. We adjudicated the quality and detail of the phenotype data via 46 questions pertaining to six priority domains: (I) Development, cognition, and mental health; (II) Feeding and growth; (III) Medication use and treatment history; (IV) Pain, sleep, and quality of life; (V) Adulthood; and (VI) Epilepsy. For a subset of articles, all subsequent published follow-up case descriptions were identified and assessed in a similar manner. A modified Delphi approach was used to develop consensus reporting guidelines, with input from content experts across four countries. In total, 200 of 3243 screened publications met inclusion criteria. Relevant phenotypic details across each of the 6 domains were rated superficial or deficient in >87% of papers. For example, less than 10% of publications provided details regarding neuropsychiatric diagnoses and “behavioural issues”, or about the type/nature of feeding problems. Follow-up reports (n = 95) rarely contributed this additional phenotype data. In summary, phenotype information relevant to clinical management, genetic counselling, and the stated priorities of patients and families is lacking for many newly described genetic diseases. The PHELIX (PHEnotype LIsting fiX) reporting guideline checklists were developed to improve phenotype reporting in the genomic era.

The best watering hole in town: Characteristics of ponds used by an endangered bat in an urbanizing boreal landscape

Small waterbodies, or ‘ponds’, have an important role in maintaining biodiversity and ecosystem services, particularly where freshwater sources are limited, such as arid or some urban environments. However, studies that examine the use of ponds by terrestrial wildlife are limited. In the boreal forest, aquatic habitats are important drinking and feeding habitat for endangered little brown bats (Myotis lucifugus), and their relative value likely varies based on their characteristics, such as size, adjacent land cover type, anthropogenic disturbance, and proximity to other water bodies. We examined the characteristics of ponds used by little brown bats along an urban-rural gradient in Yukon, Canada. We used ultrasonic detectors to sample 99 ponds and generalized linear mixed models to determine whether ponds with certain characteristics received more bat activity than others. Ponds were important habitat for little brown bats, as 98% of the ponds we sampled were used. Bats selected ponds based on local rather than landscape level factors, and there was less bat activity at ponds surrounded by additional open water/wetland habitat, which was contrary to our prediction. Ponds that were surrounded by additional open water/wetland habitat may have been too exposed for bats at high latitudes, where nights are short and not completely dark. Isolated ponds that are darker, such as those surrounded by mature forest, may be particularly valuable for little brown bats at high latitudes that exhibit risk-sensitive foraging. We suggest that ponds of comparatively high value for endangered little brown bats be further identified and that these ponds be protected from development, draining, and degradation, and the surrounding mature forest remains intact. More broadly, recognition and conservation of ponds and their surroundings as key habitat for species of bats requires further attention. Understanding the characteristics of ponds selected by threatened bats can help inform conservation priorities and measures that ensure the aquatic habitats they require are maintained in developing landscapes.

A Flood Season Division Model Considering Uncertainty and New Information Priority

A Flood Season Division Model Considering Uncertainty and New Information Priority

Модель информационно-телекоммуникационного взаимодействия между автоматизированными транспортными средствами в условиях неполного покрытия мобильными сетями

Objective: in order to improve safety, as well as optimize the path of movement in everyday life to reduce the time on the road, there are now more and more prerequisites for the introduction of highly automated and fully automated vehicles into various spheres of human life. This will optimize and improve the road system of the country as a whole, and most importantly — reduce accidents and, consequently, improve road safety. When implementing these vehicles, it is necessary to develop an algorithm for communication between different road users under different conditions and distances between objects. Methods: this study uses simulation modeling methods, the method of tabular and graphical data display, the method of factor analysis, methods of model experiment, as well as the method of expert assessments. Results: within the framework of the work, an algorithm has been developed for transmitting messages with different content and priority information between two highly automated / fully automated vehicles or a vehicle with an automated state infrastructure using various communication channels, as well as a simulation model based on it, which was created in the AnyLogic modeling environment. The use of different priorities allows you to guarantee the minimum delivery time of the most important messages containing information for vehicle management. As a result of the simulation, histograms of the probabilistic-temporal characteristics of the transmission of messages of various priorities and messages confirming their delivery were obtained using various communication channels. Practical significance: the simulation results make it possible to assess the quality and time of message delivery under specified conditions and to ensure effective transmission of information by changing the intensity and size of messages, the use of various communication channels and their speeds, and so on.

Grey prediction of carbon emission and carbon peak in several developing countries

The carbon emissions in BRICS countries (Brazil, Russia, India, China and South Africa) account for nearly half of global carbon emissions. To explore the future carbon emissions in BRICS countries and the carbon peak in China and India, the grey Gompertz model with new information priority accumulation is proposed. Intelligent algorithms are used in the parameter optimization. The new model not only increases the influence weight of new information on the predicted results, but also changes the predicted trend of traditional grey prediction model. The effectiveness of the new model for the carbon emission forecast is verified through the known data set. According to the predicted results, the carbon emissions in Russia, Brazil, South Africa tend to stabilize over the long term. The carbon emissions in China and India are rising slowly. China will achieve the carbon peak in 2029 with 10.2 billion tons of carbon emissions. India will not achieve the carbon peak by 2030. The current situations of the carbon emissions in BRICS countries are analyzed. It is concluded that the energy structure, industrial development and the urbanization need to be paid attention to. Detailed policies and suggestions are given in the fourth section. The above research results indicate that the new model can predict the limit value of the carbon emission. The predicted results can provide data support for the policy formulation in carbon reduction.

Социальный и ценностный смысл недоверия к гуманитарному знанию

This article analyses the urgent issue of the growing distrust of humanities knowledge. This problem is important both for those who create, systematize and broadcast humanities knowledge and for social actors whose legitimacy of actions is based on humanities knowledge. Moreover, this issue is of particular significance to those involved in public administration and education. Trust in knowledge is characterized by the recognition of its truth in the epistemological sense and of its positive meaning in the axiological sense. From this point of view, the social distrust of humanities knowledge can be explained by the socio-economic processes that generate alienation, which forms an objective basis for distrust. Narrow specialization due to the deepening division of labour, as well as technicism, consumerism and the priority of information and information technology give rise to the distrust in humanities knowledge as an expression of truth. Specifically, this is manifested in the lack of trust between wielders of power and bearers of humanities knowledge, between representatives of technical and humanities knowledge, and between actors of humanities knowledge itself, as well as in society’s deepening distrust of scholarship in general. On the whole, the integral social meaning of the growing distrust in humanities knowledge lies in the increasing subordination of humans to modern sociotechnical systems, whose elements they are becoming. The axiological component of distrust of humanities knowledge is related to its value meanings. Firstly, humanities knowledge necessarily includes value components, which causes distrust among social actors who are guided by different or opposing ideas. Secondly, humanities knowledge is inherently pluralistic, reflecting the manifoldness of human existence. Since the social and value determinants of distrust in humanities knowledge are intensifying in modern society, the article concludes that this process is objective. The results of the study may have a positive impact on the practical and cognitive activities of social actors involved in interaction causing trust and distrust.

A Standardized Benchmarking Framework to Assess Downscaled Precipitation Simulations

Abstract Presently, there is no standardized framework or metrics identified to assess regional climate model precipitation output. Because of this, it can be difficult to make a one-to-one comparison of their performance between regions or studies, or against coarser-resolution global climate models. To address this, we introduce the first steps toward establishing a dynamic, yet standardized, benchmarking framework that can be used to assess model skill in simulating various characteristics of rainfall. Benchmarking differs from typical model evaluation in that it requires that performance expectations are set a priori. This framework has innumerable applications to underpin scientific studies that assess model performance, inform model development priorities, and aid stakeholder decision-making by providing a structured methodology to identify fit-for-purpose model simulations for climate risk assessments and adaptation strategies. While this framework can be applied to regional climate model simulations at any spatial domain, we demonstrate its effectiveness over Australia using high-resolution, 0.5° × 0.5° simulations from the CORDEX-Australasia ensemble. We provide recommendations for selecting metrics and pragmatic benchmarking thresholds depending on the application of the framework. This includes a top tier of minimum standard metrics to establish a minimum benchmarking standard for ongoing climate model assessment. We present multiple applications of the framework using feedback received from potential user communities and encourage the scientific and user community to build on this framework by tailoring benchmarks and incorporating additional metrics specific to their application. Significance Statement We introduce a standardized benchmarking framework for assessing the skill of regional climate models in simulating precipitation. This framework addresses the lack of a uniform approach in the scientific community and has diverse applications in scientific research, model development, and societal decision-making. We define a set of minimum standard metrics to underpin ongoing climate model assessments that quantify model skill in simulating fundamental characteristics of rainfall. We provide guidance for selecting metrics and defining benchmarking thresholds, demonstrated using multiple case studies over Australia. This framework has broad applications for numerous user communities and provides a structured methodology for the assessment of model performance.

A novel structure adaptive discrete grey Bernoulli prediction model and its applications in energy consumption and production

It is well known that energy forecasts play an important role in guiding energy policy, economic development and technological progress. Therefore, based on the purpose of energy consumption and production forecasting, this paper proposes a novel structure adaptive discrete grey Bernoulli model, which is innovative in terms of both accumulated generating operator and model structure. In terms of accumulated generating operator, a new fractional order accumulated generating operator is proposed in this paper. The new accumulated generating operator has a different information priority by adjusting the values of the parameters. In terms of model structure, a novel discrete grey Bernoulli model is proposed in this paper. The novel model is well adapted to time series data containing nonlinear information, and can well mine and utilize the information contained in the original data. In addition, the Particle Swarm Optimization (PSO) algorithm was chosen to optimize the model parameters based on algorithm comparison experiments. This enables the model to flexibly adapt to a variety of complex data and has the ability of structure adaptive. Moreover, this paper conducts comparative experiments between the novel model and eight other forecasting algorithms for time series data. The numerical results show that the novel model has better forecasting performance for the data of China’s total energy consumption, China’s total electricity generation and China’s total domestic electricity consumption. In addition, for the model reliability problem caused by the optimization algorithm, the stability and accuracy of the model are verified by Monte Carlo simulation and probability density visualization analysis. Finally, the proposed model predicts the future development trend of energy consumption and production in China.

Digitization and globalization: Navigating the impact on social capital, natural resources, and environmental sustainability

The impact of digitization in the context of globalization on the formation and preservation of social capital, natural resources, and the environment has been considered. In examining state strategies towards digitization and its influence on social capital, principles such as specificity, integrity, historicism, and rationality were employed, along with methods of comparison and constructive criticism, as well as categories of essence, necessity, and measure. Historical, anthropological, ontological, political, axiological, and dialectical methods played a significant role in reflecting the characteristics of the information age, defining strategies, and priorities for the development of states and social capital. Countries that vigorously integrate digitization into human existence tend to develop faster, reap economic benefits, thereby contributing to the preservation of social capital. Additionally, the processes of digitization become essential for creating a digital space for the formation of digital social capital and the digital individual, each possessing unique characteristics. The digitization of social capital processes entails the introduction of digital technologies into the ontological environment of individuals, where they become part of human reality. Both positive and negative aspects of digitization have been identified, with the impact of digitization on social capital formation being complex and multifaceted, depending on the state’s development strategy in this direction. Digital technologies facilitate communication, increase access to information and resources, promoting the development of social connections and cooperation, ultimately enabling quick responses to environmental challenges for the preservation of the environment. Digitization is one of the characteristics of a society of sustainable development. Within the concept of this society, it allows accumulating social capital, developing the economy, taking into account environmental requirements. From a practical point of view, digitalization is a condition for structural changes in organizations, as a result of which the processes of internal and external communication and management change. The development of a digitalized society, in which the accumulation of social capital takes place, has a positive effect on the implementation of the principles of sustainable development: the combination of nature conservation and social development; satisfaction of basic human needs; achieving equality and social justice; ensuring social self-determination and cultural diversity; maintaining the integrity of ecosystems.

THE RUSSIAN FEDERATION AS AN OBJECT OF FOREIGN POLICY ANALYSIS OF MODERN GERMANY

The relevance of the study is due to the increasing role of the information factor in modern conditions. The amount of information is rapidly increasing, military methods of warfare have given way to information warfare, and even more countries are including ensuring information security in the list of national interests in their strategy. The article emphasizes that Germany is no exception: the country has an established, developed system of information and analytical support for decision makers, which consists of several elements: private, controlled analytical structures and party funds. The author notes that the research topics of information and analytical centers are devoted to various current areas of foreign policy. One of the main priorities of information and analytical reports at the present stage is the Russian Federation. The article identifies the main areas of research in Russia: military operation in Ukraine, the energy crisis, the current state of security policy, internal problems of Russian society, Russia’s relations with other actors in the international arena. The significance of the scientific article is in the need to take into account the results of the work of information and analytical centers of Germany when forming Russia's foreign policy in the German direction.

Using resiliency, redundancy, and representation in a Bayesian belief network to assess imperilment of riverine fishes

AbstractConservation prioritization frameworks are used worldwide to identify species at greatest risk of extinction and to allocate limited resources across regions, species, and populations. Conservation prioritization can be impeded by ecological knowledge gaps and data deficiency, especially in freshwater species inhabiting highly complex aquatic ecosystems. Therefore, we developed a flexible approach that calculates a species' imperilment risk based on the conservation principles of resiliency, redundancy, and representation (i.e., the “three R's”). Our approach organizes data on species traits, distributions, population connectivity, and threats within a Bayesian belief network capable of predicting resiliency and redundancy within representative ecological settings. Empirical data and expert judgment inform the model to provide robust and repeatable risk assessments for rare and data‐deficient species. The model calculates resiliency at hierarchical spatial scales from distributional trends and population strength. Redundancy is estimated from the connectivity and quantities of extant populations. Resiliency, redundancy, and species' inherent vulnerability based on species traits collectively estimate extirpation risk within each unique ecological setting. Extirpation risks across ecological settings characterize representation and are aggregated to estimate global imperilment risk. We demonstrate the model's utility with Piebald Madtom (Noturus gladiator), a species petitioned for listing under the U.S. Endangered Species Act. Our results revealed that resiliency, redundancy, and extirpation risks can vary spatially across the species' range while identifying populations where additional sampling could disproportionally reduce uncertainty in estimated global imperilment risk. Our approach could standardize and expedite conservation status assessments, identify opportunities for early management intervention of at‐risk species and populations, and strategically reduce uncertainty by focusing monitoring and research on priority information gaps.

A new adaptive grey seasonal model for time series forecasting tasks

PurposeThis paper intends to construct a new adaptive grey seasonal model (AGSM) to promote the application of the grey forecasting model in quarterly GDP.Design/methodology/approachFirstly, this paper constructs a new accumulation operation that embodies the new information priority by using a hyperparameter. Then, a new AGSM is constructed by using a new grey action quantity, nonlinear Bernoulli operator, discretization operation, moving average trend elimination method and the proposed new accumulation operation. Subsequently, the marine predators algorithm is used to quickly obtain the hyperparameters used to build the AGSM. Finally, comparative analysis experiments and ablation experiments based on China's quarterly GDP confirm the validity of the proposed model.FindingsAGSM can be degraded to some classical grey prediction models by replacing its own structural parameters. The proposed accumulation operation satisfies the new information priority rule. In the comparative analysis experiments, AGSM shows better prediction performance than other competitive algorithms, and the proposed accumulation operation is also better than the existing accumulation operations. Ablation experiments show that each component in the AGSM is effective in enhancing the predictive performance of the model.Originality/valueA new AGSM with new information priority accumulation operation is proposed.

CARA WIMBA DAN TATA UNGKAPAN BAHASA RUPA DALAM PRINT-AD SEBAGAI STRATEGI KOMUNIKASI Studi Kasus Iklan Pemenang The Cannes Lions International Festival of Creativity Kategori Print-Ad pada tahun 1992

There are many advertisements whose visual expressions are dominated by Western fine arts and graphic design techniques (which tend to be one of the patrons of graphic design scholarship in Indonesia, so it requires significant exploration and development of traditional art roots to be able to find ideas about Indonesian graphic design. Wimba Method and Visual Language Expression is a wealth of world visual language, in this research it is used as a reference for visual styles based on local Indonesian traditions. Using a descriptive qualitative approach with a digital survey as a research instrument, the research objects were 43 advertisements that were winners in the print-ads category in The Cannes Lions International Festival of Creativity in 1992 (the first year the Press and Outdoor Lions category appeared). From the research results, it was concluded that visual style has the impact of providing stimuli to the target audience to determine the priority of information to be accessed; where there is a tendency, determination This begins first from an affective point of view, rather than a cognitive one.

Using entrepreneurial competencies and action to profile entrepreneurs: a CHAID analysis approach

PurposeEntrepreneurial trait and behaviour approaches are used to identify differing entrepreneurial profiles. Specifically, this study aims to determine which entrepreneurial competencies (ECs) can predict entrepreneurial action (EA) for distinct profiles, such as male versus female, start-up versus established and for entrepreneurs within different age groups and educational levels.Design/methodology/approachThe research was conducted using a survey method on a large sample of 1,150 South African entrepreneurs. Chi-squared automatic interaction detection (CHAID) algorithms were used to build decision trees to illustrate distinct entrepreneurial profiles.FindingsEach profile has a different set of ECs that predict EA, with a growth mindset being the most significant predictor of action. Therefore, this study confirms that a “one-size-fits-all” approach cannot be applied when profiling entrepreneurs.Research limitations/implicationsFrom a pedagogical standpoint, different combinations of these ECs for each profile provide priority information for identification of appropriate candidates (e.g. the highest potential for success) and training initiatives, effective pedagogies and programme design (e.g. which individual ECs should be trained and how should they be trained).Originality/valuePrevious work has mostly focused on demographic variables and included a single sample to profile entrepreneurs. This study maintains much wider applicability in terms of examining profiles in a systematic way. The large sample size supports quantitative analysis of the comparisons between different entrepreneurial profiles using unconventional analyses. Furthermore, as far as can be determined, this represents the first CHAID conducted in a developing country context, especially South Africa, focusing on individual ECs predicting EA.

Based on Zabbix-Prometheus Group Classification Alarm System

The efficient operation of the system is the foundation for improving business level in the rapid development of information technology. Based on the data integration of the Zabbix-Prometheus system, it achieves information data storage, data collection, grouping and classification linkage alarm functions. The Zabbix-Prometheus grouping and classification alarm integration system can effectively solve the problems of a large amount of redundant alarm information, no priority of alarm information, and inability to silence. It has established a multi-platform integrated alarm service system. It has implemented linkage alarm functions such as email, SMS, and WeChat.

An Stable Intelligence Medical Trolley Path Planning System Based on Improved Breadth First Search Algorithm

Intelligence medical trolley path planning system is a comprehensive intelligent system that integrates many functions such as environmental perception, decision planning and motion control. Due to the characteristics of low speed, simple operating conditions, and priority information, it is unmanned one of the first scenes that can be achieved, with high research value. Although Intelligence Medical Trolley System has been widely developed and developed, many problems still need to be resolved.For example, the search path is not the optimal path resulting in reduced efficiency, or the algorithm is not stable enough, causing the trolley to deviate from the running path.The Path Planning algorithm is the core content of the robot. This article focuses on the improvement of path planning algorithm and determine the feasibility of the algorithm through simulation experiments and optimize. The feasibility of the algorithm is confirmed and optimized by simulation experiments. Finally, through the infrared sensor and the physical sign combine the reality with the algorithm results, and verifies the path planning algorithm with the mobile robot entity. This design uses c++ as the development language, and then uses Arduino as the development platform, lets the mobile robot move according to the path which produced by the algorithm. The experimental results show that the system based on Breath First Search (BFS) algorithm is feasible and can be applied to the working environment of hospital.

Systematic Violence Monitoring to Reduce Underreporting and to Better Inform Workplace Violence Prevention Among Health Care Workers: Before-and-After Prospective Study.

Monitoring workplace violence (WPV) against health care workers (HCWs) through incident reporting is crucial to drive prevention, but the actual implementation is spotty and experiences underreporting. This study aims to introduce a systematic WPV surveillance in 2 public referral hospitals in Italy and assess underreporting, WPV annual rates, and attributes "before" (2016-2020) and "after" its implementation (November 2021 to 2022). During 2016-2020, incident reporting was based on procedures and data collection forms that were neither standardized between hospitals nor specific for aggressions. We planned and implemented a standardized WPV surveillance based on (1) an incident report form for immediate and systematic event notification, adopting international standards for violence definitions; (2) second-level root cause analysis with a dedicated psychologist, assessing violence determinants and impacts and offering psychological counseling; (3) a web-based platform for centralized data collection; and (4) periodic training for workforce coordinators and newly hired workers. We used data from incident reports to estimate underreporting, defined as an observed-to-expected (from literature and the "before" period) WPV ratio less than 1, and the 12-month WPV rates (per 100 HCWs) in the "before" and "after" periods. During the latter period, we separately estimated WPV rates for first and recurrent events. In the "before" period, the yearly observed-to-expected ratios were consistently below 1 and as low as 0.27, suggesting substantial violence underreporting of up to 73%. WPV annual rates declined in 1 hospital (from 1.92 in 2016 to 0.57 in 2020) and rose in the other (from 0.52 to 1.0), with the divergence being attributable to trends in underreporting. Available data were poorly informative to identify at-risk HCW subgroups. In the "after" period, the observed-to-expected ratio rose to 1.14 compared to literature and 1.91 compared to the "before" period, consistently in both hospitals. The 12-month WPV rate was 2.08 (95% CI 1.79-2.42; 1.52 and 2.35 in the 2 hospitals); one-fifth (0.41/2.08, 19.7%) was due to recurrences. Among HCWs, the youngest group (3.79; P<.001), nurses (3.19; P<.001), and male HCWs (2.62; P=.008) reported the highest rates. Emergency departments and psychiatric wards were the 2 areas at increased risk. Physical assaults were more likely in male than female HWCs (45/67, 67.2% vs 62/130, 47.7%; P=.01), but the latter experienced more mental health consequences (46/130, 35.4% vs 13/67, 19.4%; P=.02). Overall, 40.8% (53/130) of female HWCs recognized sociocultural (eg, linguistic or cultural) barriers as contributing factors for the aggression, and 30.8% (40/130) of WPV against female HCWs involved visitors as perpetrators. A systematic WPV surveillance reduced underreporting. The identification of high-risk workers and characterization of violence patterns and attributes can better inform priorities and contents of preventive policies. Our evaluation provides useful information for the large-scale implementation of standardized WPV-monitoring programs.

The Discrete Grey Power Model Based on the New Information Priority and its Application

How to upgrade the performance and suitability of the forecasting method and reduce the modelling error for nonlinear small sample data series has been one of the most key problems in grey system theoretical exploration. The main work of this study, a discrete grey power model is formulated that can not only simulate the development of exponential and power function systems but can also reflect the interaction between them. The new model not only ensures that the more recent information is given some priority but exhibits a lower simulation error and greater prediction accuracy. The findings indicated that the presented model minimizes the average relative error in theory, with better adaptability than previous models in simulation and prediction. The case study demonstrates that the new model is more appropriate for predicting small sample data than the previous grey power model. This research addresses limitations in existing grey models and expands the grey model system.