Multiple Event Types Research Articles

Dissociative Subtype of Posttraumatic Stress Disorder and its Correlates Among Treatment-Seeking Refugees

ABSTRACT The dissociative subtype of posttraumatic stress disorder (PTSD-DS) denotes a severe type of PTSD associated with complex trauma exposure and psychiatric comorbidity. Refugees may be at heightened risk of developing PTSD-DS, but research is lacking. This cross-sectional study aimed to examine PTSD-DS and its demographic, trauma-related, and clinical correlates among a convenience sample of refugee patients over 18 years old who were diagnosed with PTSD according to DSM-5. PTSD-DS (Clinician-Administered PTSD Scale for DSM-5), trauma exposure (Life Events Checklist for DSM-5) and general psychopathology (Brief Symptom Inventory) were assessed at intake. T-tests, chi-square tests, and logistical regression analysis were conducted. The final sample consisted of 552 participants (177 (32.1%) women; 375 (67.9%) men; average age 40.0 years (SD = 11.2)) who originated from 63 countries. Of those, 158 (28.6%) met criteria for PTSD-DS. Participants with PTSD-DS scored significantly higher on PTSD symptom severity (t(550)=-5.270, p < .001), number of traumatic event types (t(456)=-3.499, p < .001), and exposure to sexual assault (χ(1) = 6.471, p = .01) than those without PTSD-DS. The odds of having PTSD-DS increased by 14.1% with exposure to each additional traumatic event type (OR = 1.141, CI 0.033–1.260). In conclusion, around 29% of adult treatment-seeking refugees with PTSD met the criteria for PTSD-DS. Those exposed to multiple traumatic event types including sexual assault, regardless of sex, were especially at risk. Having PTSD-DS was associated with more severe PTSD. Prioritizing trauma-focused treatment for those with PTSD-DS is recommended.

Environmental sustainability in the event industry: a systematic review and a research agenda

Over the past few decades, the event industry has witnessed massive growth worldwide. This, however, has come with considerable environmental costs. Improper management of any type of event can indeed lead to significant environmental impacts, including increased carbon emissions, waste generation, and resource consumption. To establish a common knowledge base on the topic, we conducted a Systematic Literature Review and critically examined the current state of academic research regarding environmental sustainability in events. Our analysis reveals a fast-growing and interdisciplinary field, with 247 articles coming from multiple geographic locations and addressing multiple event types – from mega-events to small-scale festivals. We identify four thematic areas on which scholars have focused: (1) Events’ Environmental Impacts, namely the evaluation of the environmental impacts and legacy of events; (2) Green Event Management, the organization of environmentally friendly events across multiple perspectives (e.g., strategic, operational); (3) Environmental Policy & Guidelines, the contribution of policymakers and other industry stakeholders for a greener event sector; (4) Environmental Psychology of Event Stakeholders, the factors affecting perceptions and behaviours of event stakeholders in relation to environmental issues. We provide an in-depth review of these areas, identify gaps in our current understanding, and draft a comprehensive agenda to address them.

HyperED: A hierarchy‐aware network based on hyperbolic geometry for event detection

AbstractEvent detection plays an essential role in the task of event extraction. It aims at identifying event trigger words in a sentence and classifying event types. Generally, multiple event types are usually well‐organized with a hierarchical structure in real‐world scenarios, and hierarchical correlations between event types can be used to enhance event detection performance. However, such kind of hierarchical information has received insufficient attention which can lead to misclassification between multiple event types. In addition, the most existing methods perform event detection in Euclidean space, which cannot adequately represent hierarchical relationships. To address these issues, we propose a novel event detection network HyperED which embeds the event context and types in Poincaré ball of hyperbolic geometry to help learn hierarchical features between events. Specifically, for the event detection context, we first leverage the pre‐trained BERT or BiLSTM in Euclidean space to learn the semantic features of ED sentences. Meanwhile, to make full use of the dependency knowledge, a GNN‐based model is applied when encoding event types to learn the correlations between events. Then we use a simple neural‐based transformation to project the embeddings into the Poincaré ball to capture hierarchical features, and a distance score in hyperbolic space is computed for prediction. The experiments on MAVEN and ACE 2005 datasets indicate the effectiveness of the HyperED model and prove the natural advantages of hyperbolic spaces in expressing hierarchies in an intuitive way.

Events and Economic Sustainability- a critical commentary

This critical commentary focuses on the research in Event Management on economic sustainability and events. We begin with a broad definition of an economically sustainable event that builds on economic impact to include social equity, liveability and community well-being, and accounts for environmental and social costs. We consider the methods, approaches and types of events represented in the journal which lack diversity. The findings indicate that quantitative approaches predominate, focused on the direct and indirect contributions of visitor expenditure. Social, natural, and community capital have received less attention. The review calls for greater consideration of the economic sustainability of events beyond the tourism contribution; longitudinal studies on the impacts of events on the economic well-being of communities; understanding how events impact equity; correcting research bias towards Western developed economies by examining different contexts; exploring multiple event types; and investigating the role of planning in realising economically sustainable events.

Event Detection with Word-Word Relation Classification

Event detection (ED) is a critical task in information extraction, aiming to identify triggers and types. Current research has increasingly focused on fine-grained types, where a single sentence may contain multiple trigger words and event types. Previous models only considered sentence-level features, neglecting word-word features and word positional information in the text. We propose a novel labeling scheme that treats event detection as a word-word relation recognition task. In this approach, we first identify relationships between word pairs and then utilize these relationships to perform trigger and event-type detection. By adopting this method, we can efficiently and concurrently identify triggers and types in a sentence. Leveraging word pair relationships effectively addresses scenarios in which multiple trigger words appear in a single sentence. The results demonstrate that our approach outperforms several baseline models.

Concurrent Multi-Label Prediction in Event Streams

Streams of irregularly occurring events are commonly modeled as a marked temporal point process. Many real-world datasets such as e-commerce transactions and electronic health records often involve events where multiple event types co-occur, e.g. multiple items purchased or multiple diseases diagnosed simultaneously. In this paper, we tackle multi-label prediction in such a problem setting, and propose a novel Transformer-based Conditional Mixture of Bernoulli Network (TCMBN) that leverages neural density estimation to capture complex temporal dependence as well as probabilistic dependence between concurrent event types. We also propose potentially incorporating domain knowledge in the objective by regularizing the predicted probability. To represent probabilistic dependence of concurrent event types graphically, we design a two-step approach that first learns the mixture of Bernoulli network and then solves a least-squares semi-definite constrained program to numerically approximate the sparse precision matrix from a learned covariance matrix. This approach proves to be effective for event prediction while also providing an interpretable and possibly non-stationary structure for insights into event co-occurrence. We demonstrate the superior performance of our approach compared to existing baselines on multiple synthetic and real benchmarks.

Prevalence of opioid dependence in New South Wales, Australia, 2014-16: Indirect estimation from multiple data sources using a Bayesian approach.

To estimate the prevalence of, and number of unobserved people with opioid dependence by sex and age group in New South Wales (NSW), Australia. We applied a Bayesian statistical modelling approach to opioid agonist treatment records linked to adverse event rate data. We estimated prevalence from three types of adverse event separately: opioid mortality, opioid-poisoning hospitalizations and opioid-related charges. We extended the model and produced prevalence estimates from a 'multi-source' model based on all three types of adverse event data. This study was conducted in NSW, Australia, 2014-16 using data from the Opioid Agonist Treatment and Safety (OATS) study, which included all people who had received treatment for opioid dependence in NSW. Aggregate data were obtained on numbers of adverse events in NSW. Rates of each adverse event type within the OATS cohort were modelled. Population data were provided by State and Commonwealth agencies. Prevalence of opioid dependence among those aged 15-64 years in 2016 was estimated to be 0.96% (95% credible interval [CrI] = 0.82%, 1.12%) from the mortality model, 0.75% (95% CrI = 0.70%, 0.83%) from hospitalizations, 0.95% (95% CrI = 0.90%, 0.99%) from charges and 0.92% (95% CrI = 0.88%, 0.96%) from the multi-source model. Of the estimated 46 460 (95% CrI = 44 680, 48 410) people with opioid dependence in 2016 from the multi-source model, approximately one-third (16 750, 95% CrI = 14 960, 18 690) had no record of opioid agonist treatment within the last 4years. From the multi-source model, prevalence in 2016 was estimated to be 1.24% (95% CrI = 1.18%, 1.31%) in men aged 15-44, 1.22% (95% CrI = 1.14%, 1.31%) in men 45-64, 0.63% (95% CrI = 0.59%, 0.68%) in women aged 15-44 and 0.56% (95% CrI = 0.50%, 0.63%) in women aged 45-64. A Bayesian statistical approach to estimate prevalence from multiple adverse event types simultaneously calculates that the estimated prevalence of opioid dependence in NSW, Australia in 2016 was 0.92%, higher than previous estimates.

Advancing document-level event extraction: Integration across texts and reciprocal feedback.

The primary objective of document-level event extraction is to extract relevant event information from lengthy texts. However, many existing methods for document-level event extraction fail to fully incorporate the contextual information that spans across sentences. To overcome this limitation, the present study proposes a document-level event extraction model called Integration Across Texts and Reciprocal Feedback (IATRF). The proposed model constructs a heterogeneous graph and employs a graph convolutional network to enhance the connection between document and entity information. This approach facilitates the acquisition of semantic information enriched with document-level context. Additionally, a Transformer classifier is introduced to transform multiple event types into a multi-label classification task. To tackle the challenge of event argument recognition, this paper introduces the Reciprocal Feedback Argument Extraction strategy. Experimental results conducted on both our COSM dataset and the publicly available ChFinAnn dataset demonstrate that the proposed model outperforms previous methods in terms of F1 value, thus confirming its effectiveness. The IATRF model effectively solves the problems of long-distance document context-aware representation and cross-sentence argument dispersion.

Visitor Behaviors Can Influence the Risk of Patient Harm: An Analysis of Patient Safety Reports From 92 Hospitals

Background: Previous research has shown that visitors can decrease the risk of patient harm; however, the potential to increase the risk of patient harm has been understudied. Methods: We queried the Pennsylvania Patient Safety Reporting System database to identify event reports that described visitor behaviors contributing to either a decreased or increased risk of patient harm. Event reports from January 1 to June 30, 2019, were searched for keywords and reviewed for inclusion criteria. Event reports were manually coded to identify visitor influence on risk of patient harm (e.g., increase or decrease), visitor behavior, and event type. Results: A total of 427 event reports were analyzed and we identified five categories of visitor behavior that influenced patient safety by either decreasing or increasing the risk of patient harm. We found that 63.7% (272 of 427) of event reports described a visitor behavior that decreased the risk of patient harm and the remaining 36.3% (155 of 427) of reports described behavior that increased the risk of harm. There was a greater variety of visitor behaviors that contributed to an increased risk of patient harm, as opposed to a decreased risk of harm. The visitor behavior most frequently associated with a decreased risk of patient harm was communicating with staff (91.2%, 248 of 272); for example, to inform staff of deterioration of a patient’s condition. The visitor behavior most frequently associated with an increased risk of patient harm was moving a patient (63.2%, 98 of 155). Across the 427 event reports, we found that visitor behavior was associated with seven event types; the falls event type (61.6%, 263 of 427) and medication-related event type (14.8%, 63 of 427) occurred most frequently. Conclusion: The current study provides insight into which visitor behaviors are contributing to a decreased risk of patient harm and adds to the literature by identifying behaviors that can increase the risk of patient harm, across multiple event types. Table 6 and Table 7 outline potential safety strategies that staff and facilities may consider using to target visitor behavior. As outlined in Table 6, the use of warning and instructional signage can be a relatively low-effort and effective strategy to influence visitor behavior and address multiple behavior categories and event types.

A NOVEL TRUE REAL-TIME SPATIOTEMPORAL DATA STREAM PROCESSING FRAMEWORK

The ability to interpret spatiotemporal data streams in real-time is critical for a range of systems. However, processing vast amounts of spatiotemporal data out of several sources, such as online traffic, social platforms, sensor networks, and other sources, is a considerable challenge. The major goal of this study is to create a framework for processing and analyzing spatiotemporal data from multiple sources with irregular shapes so that researchers can focus on data analysis instead of worrying about the data sources' structure. We introduced a novel spatiotemporal data paradigm for true-real-time stream processing, which enables high-speed and low-latency real-time data processing, with these considerations in mind. A comparison of two state-of-the-art real-time process architectures was offered, as well as a full review of the various open-source technologies for real-time data stream processing, and their system topologies were also presented. Hence, this study proposed a brand-new framework that integrates Apache Kafka for spatiotemporal data ingestion, Apache flink for true real-time processing of spatiotemporal stream data, as well as machine learning for real-time predictions, and Apache Cassandra at the storage layer for distributed storage in real-time. The proposed framework was compared with others from the literature using the following features: Scalability (Sc), prediction tools (PT), data analytics (DA), multiple event types (MET), data storage (DS), Real-time (Rt), and performance evaluation (PE) stream processing (SP), and our proposed framework provided the ability to handle all of this task.

A Joint Learning Framework for the CCKS-2020 Financial Event Extraction Task

This paper presents a winning solution for the CCKS-2020 financial event extraction task, where the goal is to identify event types, triggers and arguments in sentences across multiple event types. In this task, we focus on resolving two challenging problems (i.e., low resources and element overlapping) by proposing a joint learning framework, named SaltyFishes. We first formulate the event extraction task as a joint probability model. By sharing parameters in the model across different types, we can learn to adapt to low-resource events based on high-resource events. We further address the element overlapping problems by a mechanism of Conditional Layer Normalization, achieving even better extraction accuracy. The overall approach achieves an F1-score of 87.8% which ranks the first place in the competition.

SEEFT: Planned Social Event Discovery and Attribute Extraction by Fusing Twitter and Web Content

Social events comprise some of the most popular topics in social media. Automatically identifying planned social events and extracting structured information, such as event title, date, and location, would enable more effective index, display and search for social events. However, the informal and noisy nature of language used in social media can degrade the quality of event extraction, resulting in broken titles, incorrect or absent attributes - making the resulting event databases not suitable for realistic applications. Previous work mostly focused on event identification and categorization in Twitter. Yet, event title extraction, arguably one of the most useful and difficult tasks in this domain, has never been investigated. In this paper, we address the task of identifying and extracting structured information (titles, dates, locations) for planned social events, and introduce SEEFT, a social event extraction system, which uses social media content to discover events. To extract the event title and other attributes, SEEFT fuses the original social media content and the content of other Tweets and webpages. Experiments over multiple popular event types and more than a thousand of event instances show that SEEFT significantly outperforms the previous state-of-the-art system in event identification. Moreover, by fusing information from multiple sources, SEEFT is able to extract event titles with high accuracy, providing the foundation for practical applications such as event discovery, search, and recommendation.

Polyvictimization and Psychosocial Outcomes Among Trauma-Exposed, Clinic-Referred Youth Involved in the Juvenile Justice System

Polyvictimization is a robust predictor of emotional and behavioral problems and is linked to involvement in juvenile justice and other public sector systems. This study extends prior research by employing person-centered methods for identifying polyvictimization patterns among trauma-exposed, clinic-referred, justice-involved youth (n = 689; ages 12-18 years) and how identified classes differ on psychosocial outcomes and demographic characteristics. Most participants had experienced multiple traumatic event (TE) types. Latent class analyses identified three classes: mixed trauma/bereavement exposure group (55.1%; Mean = 3.0 TE types); maltreatment polyvictimized group (29.3%; Mean = 5.7 TE types); and maltreatment plus extreme violence polyvictimized group (15.7%; Mean = 9.3 TE types). Polyvictimized youth were more likely to be female, in out-of-home placements, and experiencing negative psychosocial outcomes (e.g., Posttraumatic Stress Disorder). Hispanic/Latino youth were overrepresented in the extreme polyvictimized subgroup. Results underscore the need for cross-system coordination of trauma-informed, comprehensive services for clinic-referred, justice-involved youth.

Bankruptcy risk dependence structure using the INAR model comprising macroeconomic indicators applied to stress tests

In this study, we examined the types of bankruptcy risk dependence structures of Japanese firms. We classified bankruptcy events observed in Japan into multiple event types based on industry type and then used a multidimensional Hawkes process, which has been gaining attention recently in the field of finance research, to model the self-exciting and/or mutually exciting properties of bankruptcy among the event types. For the estimation of the intensity processes associated with the multidimensional Hawkes process, in addition to the analysis by Embrechts and Kirchner (Quantitative Finance 17:571–595, 2016) using the integer-valued auto-regression (INAR) model, we tested a new approach using the INAR model comprising macroeconomic indicators. Subsequently, we compared and considered the estimation results of the two specifications and demonstrated that the INAR model comprising macroeconomic indicators is better than the INAR model lacking macroeconomic indicators in terms of estimating the intensity process. Further, we demonstrated the effectiveness of the model from a practical perspective, with applications to stress tests.

Inverse probability weighted estimation for recurrent events data with missing category.

Modeling recurrent event data with multiple event types has drawn interest in recent biomedical studies due to its flexibility for understanding different risk factors for multiple recurrent event processes. However, in such data type, missing event type appears frequently because of various reasons such as recording ignorance or resource limitation. In this study, we aim to propose an inverse probability weighted estimation that is commonly used in the missing data literature to correct possibly biased estimation by a complete-case analysis. This approach is not limited to a specific form of the recurrent event model. We derive the large sample theory in a general form. We demonstrate that our approach can be applied to either multiplicative or additive rates model with practical sample size via comprehensive simulations. Nonmucoid and mucoid Pseudomonas aeruginosa infections of 14888 patients in 2016 Cystic Fibrosis Foundation Patient Registry data are analyzed to show that, without including 12% events with missing event type in the analysis, several factors may be misidentified as risk factors for the nonmucoid type of infections.

Analysis of cyclic recurrent event data with multiple event types.

Recurrent event data frequently arise in practice, and in some cases, the event process has cyclic or periodic components. We propose a semiparametric rate model with multiple event types that have such features. Generalized estimating equations are used for the estimation of regression coefficients after profiling the baseline rate function with a fully nonparametric estimator. The proposed estimators are shown to be consistent and asymptotically Gaussian. Their finite-sample behavior is assessed through simulation experiments. The predictability of the model with and without the cyclic component is also compared. With the cyclic component, our model improves the predictability of a conventional model without the cyclic feature. Data on recurrent fire alarms in Blenheim, New Zealand, are used for illustration purposes.

Social cognition in context: A naturalistic imaging approach

Social processing occurs within dynamic, complex, and multimodal contexts, but the study of social cognition typically involves static, artificial stimuli. Naturalistic approaches (e.g., movie viewing) can recapture the richness and complexity of real-world interactions. Novel analytic approaches allow for the investigation of functional brain organization in response to contextually embedded and extended events with a complex temporal structure during movie viewing or narrative processing. In addition to these within-brain measures, movies afford between-brain analyses such as inter-subject correlation, which allows for identification of stimulus-specific brain response through the correlation of brain activity between participants’ brains. Research using these approaches offers both practical and theoretical advantages in understanding how we navigate our social world. Practically, movies are engaging stimuli that allow for more rapid presentation of multiple event types and improve compliance even in very young populations. Theoretically, studies have validated the use of these measures by demonstrating functional selectivity to contextually embedded stimuli. Naturalistic approaches also allow for novel insights. For example, regions associated with social cognition have longer temporal receptive windows, making them well suited to social-cognitive processes that require integration of information over longer timescales. Furthermore, the similarity in the temporal and spatial brain response between individuals during naturalistic viewing is related to age, predictive of friendships, and reduced in autism spectrum disorder. These findings offer first glimpses into the power of using these naturalistic, dynamic approaches to understand how we perceive, reason about, and interact with others.

Planning and analyzing clinical trials with competing risks: Recommendations for choosing appropriate statistical methodology.

In the analysis of time-to-event data, competing risks occur when multiple event types are possible, and the occurrence of a competing event precludes the occurrence of the event of interest. In this situation, statistical methods that ignore competing risks can result in biased inference regarding the event of interest. We review the mechanisms that lead to bias and describe several statistical methods that have been proposed to avoid bias by formally accounting for competing risks in the analyses of the event of interest. Through simulation, we illustrate that Gray's test should be used in lieu of the logrank test for nonparametric hypothesis testing. We also compare the two most popular models for semiparametric modelling: the cause-specific hazards (CSH) model and Fine-Gray (F-G) model. We explain how to interpret estimates obtained from each model and identify conditions under which the estimates of the hazard ratio and subhazard ratio differ numerically. Finally, we evaluate several model diagnostic methods with respect to their sensitivity to detect lack of fit when the CSH model holds, but the F-G model is misspecified and vice versa. Our results illustrate that adequacy of model fit can strongly impact the validity of statistical inference. We recommend analysts incorporate a model diagnostic procedure and contingency to explore other appropriate models when designing trials in which competing risks are anticipated.

Prevalence and co-existence of morbidity of posttraumatic stress and functional impairment among Burundian refugee children and their parents

ABSTRACT Background: Although the family constitutes the prime source of risk and resilience for the well-being of children growing up in adverse conditions, the mental health of children living in refugee camps has rarely been investigated in conjunction with their parents’ mental health. Objectives: To examine the prevalence of posttraumatic stress disorder (PTSD) and other mental health problems among Burundian refugee children and their parents living in Tanzanian refugee camps and to identify patterns of comorbidity among children and their parents based on PTSD symptom levels and functional impairment. Methods: We recruited a representative sample of 230 children aged 7–15 years and both of their parents (n = 690) and conducted separate structured clinical interviews. Latent Class Analysis was applied to identify patterns of comorbidity. Results: Children and parents were exposed to multiple traumatic event types. In total, 5.7% of children fulfilled DSM-5 criteria for PTSD in the past month and 10.9% reported enhanced levels of other mental health problems. 42.6% indicated clinically significant functional impairment due to PTSD symptoms. PTSD prevalence was higher among mothers (32.6%) and fathers (29.1%). Latent Class Analysis (LCA) revealed a familial accumulation of PTSD symptoms as children with high symptom levels and impairment were likely to live in families with two traumatized parents. Conclusions: Although the number of children who need support for trauma-related mental health problems was relatively low, taking into account parental trauma could aid to identify at-risk children with elevated PTSD symptom levels and impairment even in the face of existing barriers to mental health care access for children in refugee camp settings (e.g. lack of targeted services, prioritization of managing daily stressors).

Hypovitaminosis D Is Not A Risk Factor for Stress Fracture or Reaction in NCAA Division I Track And Field

Objectives:Most research on the role of vitamin D on skeletal health has been dedicated to the elderly for fracture prevention. However, there is a paucity of research examining the role of vitamin D in elite track and field athletes. The objective of this study was to evaluate the prevalence of stress reactions and fractures in NCAA Division I track and field athletes, and to determine the relationship between fractures and serum vitamin D levels.Methods:After IRB approval, we evaluated data for men’s and women’s outdoor track and field teams from 2015-2018 at a large NCAA Division I athletics program. Vitamin D levels were drawn during pre-season annual physicals. The cutoff for hypovitaminosis D was set at 40 nmol/L. Demographic and athletic tenure data were recorded and analyzed, and competitive events were grouped into one of four categories: 1) running events, 2) jumping events, 3) throwing events, and 4) multiple event types. Student T tests, chi-squared tests, and multivariate logistic regression were performed.Results:Of the 148 athletes analyzed, greater than 50% had hypovitaminois D (Figure 1). There were 29 (19.6%) athletes sustained a stress fracture or stress reaction. There was no difference in mean vitamin D levels in the injured group vs the non-injured group (43.55 ± 11.97 vs. 39.61 ± 12.32, respectively; p=0.122). Additionally, multivariate logistic regression failed to demonstrate an association between stress fracture or reaction incidence and vitamin D status, age, BMI, or event type (p>0.05 for all).Conclusion:These data suggest that NCAA Division I track and field athletes suffer a high rate of stress fractures and reactions, though those with hypovitaminosis D are not at increased risk of these injuries. This is the first study to demonstrate this in a heterogeneous population of elite athletes. Future studies investigating other modifiable risk factors for fracture in this population are warranted to prevent the health and athletic consequences of these fractures.