Measures Of Cohesion Research Articles

Attributional consensus: The importance of agreement over causes for team performance to interpersonal outcomes and performance

ObjectivesInvestigate (a) the effects of attributional consensus on interpersonal outcomes and performance, (b) the effects of attribution type (i.e., adaptive/maladaptive) on performance, and (c) the interaction effects between attributional consensus and attribution type on performance. DesignAcross two studies (i.e., vignette and behavioural experiments), independent samples t-tests were used to examine the main effects of attributional consensus on interpersonal outcomes. A 2 (attributional consensus: high, low) x 2 (attribution type: adaptive, maladaptive) x 2 (time: pre, post) ANOVA with repeated measures on the last factor was used to analyse the main and interaction effects of attributional consensus and attribution type on performance. MethodIn Study 1, participants (N = 100) read a vignette describing a hypothetical situation in which they and their partner agreed or disagreed over an adaptive or maladaptive attribution. They then completed measures of conflict and cohesion. In Study 2, participants (N = 56) completed an experiment in which they performed a dart throwing task with a partner (a confederate) and were subsequently told they failed the task. After selecting an adaptive or maladaptive attribution, the confederate then agreed or disagreed with the participant. Measures of conflict, cohesion, social identity, and performance were then taken. ResultsHigh attributional consensus led to lower levels of conflict and higher levels of cohesion and social identity. Further, regardless of attribution type, high attributional consensus led to better performance. ConclusionOverall the results provide evidence for the positive effects of high attributional consensus on interpersonal and performance outcomes.

3144 Exploring communication and collaboration at the Mount Sinai Health Hackathon: a social network analysis

OBJECTIVES/SPECIFIC AIMS: The study aims to (1) investigate the structural patterns of professional communication that exist at the Mount Sinai Health Hackathon (2) explore if and how the professional networks of the participants change after engaging in the Mount Sinai Health Hackathon (3) explore any associations between the characteristics of participants’ professional networks and successful innovation development. METHODS/STUDY POPULATION: The recruitment pool consists of all 78 Mount Sinai Health Hackathon 2018 participants. Characteristics of the social network of Health Hackathon participants are assessed via an SNA data collection instrument at three time points: T1 directly before the Health Hackathon event, T2 directly after the event, T3 six months post-event. The Icahn School of Medicine at Mount Sinai Institutional Review Board approved this study as exempt. In order to explore patterns of communication between Health Hackathon participants during event, whole network data is collected at T2. Participants are provide with a roster of Mount Sinai Health hackathon participant names and asked to report the nature, frequency and perceived importance of their interaction with each of the other participants over the duration of the 48 hour event. In order capture any network change in the wider professional networks of the individual participants, known as “ego networks”, participants are asked to complete an SNA ego network survey at time points T1, T2 and T3. Open ended questions asked participants to report up to 20 people they consider being most important to them in their professional network and record the professional background of each person, the nature of the communication and the importance of each person to their success. Finally, at T3 participants are also asked to report on their project success (determined by businesses formed, filed provisional patents, financial income generation). This will be reviewed in relation to their social network data, to see if there is any relationship between the two. Data is analyzed using the specialized SNA software, UCiNET, which creates network sociograms to visualize network data. Descriptive statistics are used to report individual-level characteristics of respondents.RESULTS/ANTICIPATED RESULTS: To describe the structural patterns of communication at each time point, the following network-level indices are calculated: density (a measure of network cohesion), degree centrality (how many connections the individual has), betweenness centrality (whether the individual provides connections to other people in a network) and closeness centrality (how close the individual is to other people in the network). Network sociograms are generated for each time point to provide a visualization of the network. To explore the hypothesis that participating in the Mount Sinai Health Hackathon increases diversity of a professional network, analysis will focus on whether and how network-level indices change pre- and post- Hackathon. It will also explore any association between network characteristics and project success. DISCUSSION/SIGNIFICANCE OF IMPACT: Social Network Analysis of the Mount Sinai Health Hackathon provides an understanding about the structure of relationships that are formed as a result of participation in this event. Although, the design of this study does not allow for inferences of causality, the SNA approach enables an in-depth exploration of Hackathon participants’ professional networks, how these evolve over time and how certain network characteristics may be associated with project success. This information will be used to inform the development, content and delivery of future Health Hackathon initatives.

Oxytocin-enhanced motivational interviewing group therapy for methamphetamine use disorder in men who have sex with men: study protocol for a randomized controlled trial

BackgroundThe prevalence of methamphetamine use disorder (MUD) in the United States has risen dramatically in the past four decades and is concentrated in populations such as men who have sex with men (MSM). Despite the public health consequences of MUD, there are no FDA-approved psychopharmacological treatments. Psychosocial treatment alone has been shown to reduce methamphetamine use, but high attrition rates limit treatment efficacy. Promising findings from animal models of MUD using exogenous oxytocin, a social neuropeptide, have set the stage for translational work. Along with unique anti-addiction effects, oxytocin holds a primary role in enhancing social salience and modulating stress. In humans, oxytocin administration, combined with evidence-based psychosocial interventions, may act synergistically to improve addiction treatment outcomes and improve retention rates in current MUD treatment.Methods/designWe are conducting a randomized, double-blind, placebo-controlled trial of oxytocin-enhanced motivational interviewing group therapy (MIGT). Oxytocin or placebo 40 IU is administered intranasally in conjunction with six, weekly MIGT sessions. We will recruit 50 MSM, initiating treatment for MUD from specialized community health programs in San Francisco, CA, USA. Individuals will be randomized (1:1) to receive six, weekly sessions of MIGT with or without oxytocin. Our primary outcome is session attendance. Other outcomes of interest include: measures of group cohesion, anxiety, psychophysiology, and stimulant craving and use.DiscussionThis will be the first study of oxytocin’s effects in humans with MUD. Findings from this novel protocol will attempt to bridge existing animal data with the need for innovative clinical treatments for MUD, inform the growing field of pharmacologically-enhanced psychotherapy, and help to elucidate mechanisms behind oxytocin’s potential anti-addiction effects.Trial registrationClinicalTrials.gov, ID: NCT02881177. Registered on 26 August 2016.

Dynamics of breast imaging research: A global scoping review and Sino-Australian comparison case study.

This study presents a quantitative analysis of global breast imaging research over the last eight decades. A dedicated Sino-Australian case study via a social network analysis (SNA) is included as China and Australia have a recent rapidly increasing number of research partnerships and strategic education/economic connections. Bibliographic data was extracted via Scopus and analysed for the social network parameters of degree centrality, closeness centrality, betweenness centrality and multiple cohesion measures in order to explore research collaboration networks at the organisational level. Within the last three decades there has been a tremendous increase in the publication rate within the scientific domain of breast imaging research, however, there is a significant lag in the development of this research area in China compared with Australia. Breast imaging research in China is considerably more insular, with less international collaboration and reduced variation between collaborators than Australia. The impact of national breast screening programs and novel cancer technologies upon collaboration networks is discussed alongside the ability of networks paradigm to reveal both frailties in research connections and to highlight networking strategies.

Network analysis of pig movements in Argentina: identification of key farms in the spread of diseases and relationship with their biosecurity level

Network analysis of pig movements in Argentina: identification of key farms in the spread of diseases and relationship with their biosecurity level

Combining structural and semantic cohesion measures to identify extract class refactoring

Class cohesion is a major design factor that affects the quality of classes. Classes that have related methods are easy to comprehend and maintain. Classes with many responsibilities are refactored by extracting some methods to new classes. This paper investigates class metrics to identify extract class refactoring opportunities to increase the degree of cohesion. An approach is presented that combines both the structural and the semantic metrics of classes to determine methods that need to be extracted in new classes. A case study is presented to evaluate the proposed approach. The aim of the study is to compare results obtained from applying semantic metrics, structural metrics, and combined metrics together. Results revealed that the proposed approach can provide a valuable set of extract class refactoring suggestions to improve class cohesion.

Combining structural and semantic cohesion measures to identify extract class refactoring

Combining structural and semantic cohesion measures to identify extract class refactoring

Resilience-based intervention for UK military recruits: a randomised controlled trial

ObjectivesWe evaluated a military resilience intervention which aimed to help UK military recruits to manage their personal health and well-being more effectively.MethodsTrainers within six pre-existing training teams were randomly allocated...

A Method for Automatically Analyzing Intelligent Tutoring System Dialogues with Coh-Metrix


 
 
 We developed a method for using Coh-Metrix to automatically analyze tutorial dialogues. Coh-Metrix, a web-based tool for automatically evaluating text, is freely available to researchers. We applied the method to 190 tutorial dialogues between women and BRCA Gist from two experiments. BRCA Gist is an intelligent tutoring system (ITS) to help women make decisions about genetic testing for breast cancer risk. Tutorial dialogues scored high on measures of textual cohesion (deep cohesion, referential cohesion, and the composite variable formality). They also scored high on measures of the situation model (LSA verb overlap and causal verb and causal particle). However, there was mixed support for the hypothesis that higher scores on Coh-Metrix variables would predict subsequent comprehension. A Coh-Metrix principle is that the observable cohesion of a text is a reliable guide to the coherence of the reader’s mental representation of that text. Thus it appears that interacting with BRCA Gist helped people form coherent mental representations of complex medical materials. We conclude that Coh-Metrix can be used to reliably assess tutorial dialogues and make inferences about the mental representations of people engaged in conversation with an ITS based on observable characteristics of the statements people make.
 
 

Cluster analysis of preoperative echocardiographic findings and outcomes following left ventricular device implantation

ObjectiveTo investigate whether preoperative echocardiography findings determine postoperative continuous-flow left ventricular assist device outcomes. MethodsFrom January 2003 to June 2017, 490 patients received a durable, continuous-flow left ventricular assist device. Two-step clustering of parameters including heart rate and preoperative echocardiographic findings (ie, left ventricular [LV] ejection fraction, right ventricular [RV] function, aortic insufficiency, mitral regurgitation [MR], tricuspid regurgitation [TR]) was performed and identified 5 distinct clusters associated with LV failure: group 1: moderate right ventricular dysfunction (RVD), severe MR and mild TR (n = 110); group 2: severe RVD, severe MR and TR (n = 64); group 3: moderate RVD and severe aortic insufficiency (n = 16); group 4: mild RVD and mild valvular pathology (n = 163); and group 5: moderate-severe RVD and mild valvular pathology (n = 137). Silhouette measure of cohesion and separation demonstrated satisfactory separation at 0.6. ResultsGroup 2 had the greatest Interagency Registry for Mechanically Assisted Circulatory Support Level 1 (25%, P = .010), preoperative right atrial pressure (11 ± 5 mm Hg, P < .001), incidence of postoperative right ventricular failure (RVF; 20%, P = .001), delayed closure of the sternum (61%, P = .002), postoperative permanent dialysis (6%, P = .04), rate of tricuspid valve repair (n = 52; 81%, P < .001), and lowest RV stroke work index (489 ± 228 cc mm Hg/m2/beat, P < .001). RVF in groups 1, 3, 4, and 5 was 6%, 0%, 4%, and 9%, respectively. No differences in incidence of heart transplantation (P = .400) or survival (P = .535) were found. Severe TR predicted RVF in those with moderate-severe preoperative RVD (P = .001, odds ratio 3.9). ConclusionsClustering demonstrated the importance of preoperative TR in predicting RVF. Combined severe LV and RV failure with severe MR and TR portends the worse prognosis.

Growth cycle synchronization of the Visegrad Four and the European Union

In this paper, we map the growth cycle synchronization across the European Union, specifically focusing on the position of the Visegrad Four countries. We study the synchronization using frequency and time–frequency domain. To accommodate for dynamic relationships among the countries, we propose a wavelet cohesion measure with time-varying weights. Analyzing quarterly data from 1995 to 2017, we show an increasing co-movement of the Visegrad Four countries with the European Union after the countries have accessed the European Union. We show that participation in a currency union increases the co-movement of the country adopting the Euro. Furthermore, we find a high degree of synchronization at business cycle frequencies of the Visegrad Four and countries of the European monetary union.

Population-specific social dynamics in chimpanzees

Understanding intraspecific variation in sociality is essential for characterizing the flexibility and evolution of social systems, yet its study in nonhuman animals is rare. Here, we investigated whether chimpanzees exhibit population-level differences in sociality that cannot be easily explained by differences in genetics or ecology. We compared social proximity and grooming tendencies across four semiwild populations of chimpanzees living in the same ecological environment over three consecutive years, using both linear mixed models and social network analysis. Results indicated temporally stable, population-level differences in dyadic-level sociality. Moreover, group cohesion measures capturing network characteristics beyond dyadic interactions (clustering, modularity, and social differentiation) showed population-level differences consistent with the dyadic indices. Subsequently, we explored whether the observed intraspecific variation in sociality could be attributed to cultural processes by ruling out alternative sources of variation including the influences of ecology, genetics, and differences in population demographics. We conclude that substantial variation in social behavior exists across neighboring populations of chimpanzees and that this variation is in part shaped by cultural processes.

A Fringe Topic in a Fragile Network

In this NSF CSforALL funded research study, the authors sought to understand the extent to which an urban district's teacher instructional support network enabled or constrained capacity to implement and diffuse Digital Literacy and Computer Science (DLCS) instructional practices throughout the K-12 curriculum. Social network analysis was used to investigate informal teacher advice-seeking and advice-giving patterns of DLCS support. Network measures of cohesion and centrality were computed. Findings revealed that DLCS-focused teacher support networks tend to exhibit very low density, have relatively few ties, include a high number of isolates (teachers with no connections), and centralize around a particular actor. In addition, a low level of overlap was found between DLCS networks and primary instructional networks. Overall, study findings suggest that teacher networks are not well-structured to support the flow of DLCS advice and support. The authors conclude that examining and strengthening teacher networks of instructional support may be a crucial step for educational leaders concerned with school improvement and the diffusion of DLCS curricula in US schools.

Clustering Neutrosophic Data Sets and Neutrosophic Valued Metric Spaces

In this paper, we define the neutrosophic valued (and generalized or G) metric spaces for the first time. Besides, we newly determine a mathematical model for clustering the neutrosophic big data sets using G-metric. Furthermore, relative weighted neutrosophic-valued distance and weighted cohesion measure, is defined for neutrosophic big data set. We offer a very practical method for data analysis of neutrosophic big data although neutrosophic data type (neutrosophic big data) are in massive and detailed form when compared with other data types.

Preliminary Evaluation of a Brief Team Cohesion Manikin Scale

The performance of small groups of individuals is substantially influenced by team cohesion. Past research has examined the processes involved in the development of cohesion, the extent to which a group of individuals are drawn together as a team, between team members across large temporal scales. Questions remain, however, regarding the progression of team cohesion within a specific set of activities. To address these questions, brief measures that are able to reliably assess team cohesion are required. In the present study, a novel set of team cohesion scales, designed to be completed rapidly and at multiple times during a task, was examined. Three team cohesion manikin scales were developed to assess interpersonal attraction, commitment, and pride. Ratings from the scales were gathered as small groups of participants completed two sets of tasks, the first in which participants observed video clips of teams that varied in displayed cohesion, and the second where groups completed two activities. High reliability was observed across the scales when participants used them to rate groups observed in video clips. During group activities, significant relations were observed between scale scores and past measures of team cohesion. The present study provides preliminary evidence that brief self-report measures can be used to assess team cohesion when observing as well as participating in group activities.

Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learning.

Protein interactions constitute the fundamental building block of almost every life activity. Identifying protein communities from Protein-Protein Interaction (PPI) networks is essential to understand the principles of cellular organization and explore the causes of various diseases. It is critical to integrate multiple data resources to identify reliable protein communities that have biological significance and improve the performance of community detection methods for large-scale PPI networks. In this paper, we propose a Multi-source Learning based Protein Community Detection (MLPCD) algorithm by integrating Gene Expression Data (GED) and a parallel solution of MLPCD using cloud computing technology. GED under different conditions is integrated with the original PPI network to reconstruct a Weighted-PPI network. To flexibly identify protein communities of different scales, we define community modularity and functional cohesion measurements and detect protein communities from WPPI. In addition, we compare the detected communities with known protein complexes and evaluate the function enrichment of protein functional modules using Gene Ontology annotations. We implement a parallel version of MLPCD on the Apache Spark platform to enhance the performance of the algorithm. Extensive experimental results indicate the superiority and notable advantages of the MLPCD algorithm over the relevant algorithms in terms of accuracy and performance.

'We pledge to improve the health of our entire community': Improving health worker motivation and performance in Bihar, India through teamwork, recognition, and non-financial incentives.

BackgroundMotivation is critical to health worker performance and work quality. In Bihar, India, frontline health workers provide essential health services for the state’s poorest citizens. Yet, there is a shortfall of motivated and skilled providers and a lack of coordination between two cadres of frontline health workers and their supervisors. CARE India developed an approach aimed at improving health workers’ performance by shifting work culture and strengthening teamwork and motivation. The intervention—“Team-Based Goals and Incentives”—supported health workers to work as teams towards collective goals and rewarded success with public recognition and non-financial incentives.MethodsThirty months after initiating the intervention, 885 health workers and 98 supervisors completed an interviewer-administered questionnaire in 38 intervention and 38 control health sub-centers in one district. The questionnaire included measures of social cohesion, teamwork attitudes, self-efficacy, job satisfaction, teamwork behaviors, equitable service delivery, taking initiative, and supervisory support. We conducted bivariate analyses to examine the impact of the intervention on these psychosocial and behavioral outcomes.ResultsResults show statistically significant differences across several measures between intervention and control frontline health workers, including improved teamwork (mean = 8.8 vs. 7.3), empowerment (8.5 vs. 7.4), job satisfaction (7.1 vs. 5.99) and equitable service delivery (6.7 vs. 4.99). While fewer significant differences were found for supervisors, they reported improved teamwork (8.4 vs. 5.3), and frontline health workers reported improved fulfillment of supervisory duties by their supervisors (8.9 vs. 7.6). Both frontline health workers and supervisors found public recognition and enhanced teamwork more motivating than the non-financial incentives.ConclusionsThe Team-Based Goals and Incentives model reinforces intrinsic motivation and supports improvements in the teamwork, motivation, and performance of health workers. It offers an approach to practitioners and governments for improving the work environment in a resource-constrained setting and where there are multiple cadres of health workers.

Collaboration Two-Way

Collaborative writing has been found to lead to more productive writing processes and enhanced final products in terms of a richer vocabulary, more accurate grammar, and better organization. The present study expands on this research strand by exploring if different group writing processes affect the quality of wiki texts composed by groups of intermediate German L2 learners. Defining true collaborative writing as involving both a balanced workload and a joint responsibility for the product from all group members, it measured collaboration in two ways. Results indicate that most of the 19 groups in this study had a somewhat unbalanced workload with wide variability in editing group members’ contributions. Although the wiki texts differed greatly with regard to quantitative measures of length, accuracy and cohesion, no correlation was found in terms of workload or co-ownership. While holistic ratings of the texts concerning accuracy and cohesion seemed at times incongruent with the analytic measures, the raters’ comments provided a perspective that captured facets and nuances of a text that the analytic indicators did not.

National Surgical Quality Improvement Program Data Indicate Blood Transfusion After Lower Extremity Bypass Increases Complication Rates

Blood transfusions have been found to increase the risk of postoperative complications in surgical patients. The aim of this study was to investigate the effect of blood transfusions on postoperative morbidity and mortality in patients undergoing lower extremity bypass procedures using the National Surgical Quality Improvement Program database. The National Surgical Quality Improvement Program database was queried for years 2014 to 2016 for lower extremity bypass and angioplasty procedures using Current Procedural Terminology codes 37220 through 37235. Cohorts were based on having received a transfusion or not. Cohorts were case matched according to age, sex, race, smoking, diabetes mellitus, hypertension, chronic obstructive pulmonary disease, congestive heart failure, chronic kidney disease, and ventilator dependence. To assess association between perioperative blood transfusions and postoperative morbidity and mortality, a binary logistic regression test in a stepwise fashion and a two-step cluster were performed. A total of 3492 patients were included in the final analysis after case matching, with 1164 receiving transfusion. Among patients who underwent lower extremity revascularization, those who received intraoperative or postoperative blood transfusion showed a higher risk of pneumonia (relative risk [RR], 3.7; P < .001), unplanned reintubation (RR, 4.3; P < .001), prolonged intubation >48 hours (RR, 4.7; P < .001), sepsis (RR, 2.8; P < .001), septic shock (RR, 5.3; P < .001), superficial surgical site infection (RR, 4.1; P < .001), deep surgical site infection (RR, 4; P = .003), organ/space surgical site infection (RR, 5.3; P = .006), acute kidney injury (RR, 7.3; P < .001), deep venous thrombosis or thrombophlebitis (RR, 2.25; P = .015), postoperative myocardial infarction (RR, 4.6; P < .001), cerebrovascular accident or stroke (RR, 2.3; P < .03), and 30-day postoperative mortality (RR, 3.6; P < .001). Of interest, the complications experienced by transfusion were predictable based on preoperative condition. Cluster quality was rated good at 0.8 based on silhouette measure of cohesion and separation. The Table describes the relationship between the patient’s preoperative profile and the odds of specific transfusion reactions. Blood transfusions increase risk of postoperative morbidity and mortality among patients who undergo lower extremity bypass procedures. Complications follow a predictable pattern based on the patient’s characteristics, which may aid in care.TableIndependent predictors of postoperative complications among blood transfusion group patientsPostoperative complication (predictor)Odds ratio95% CIP valuePneumonia COPD2.21.2-3.9.007 Congestive heart failure2.41.2-5.018Reintubation Sex (male)1.81.04-3.1.035 COPD2.41.3-4.5.004AKI Sex (male)2.71.06-6.8.036 Diabetes mellitus2.91.1-7.5.021 COPD4.51.8-10.9.001Sepsis Diabetes mellitus2.21.1-4.3.018AKI, Acute kidney injury; CI, confidence interval; COPD, chronic obstructive pulmonary disease. Open table in a new tab

(e) in Normandy: The sociolinguistics, phonology and phonetics of theLoi de Position

ABSTRACTThis article uses the pronunciation of stressed Intonational Phrase-final /ε/ and /e/ in two communities in Normandy, France, to illustrate the convergence of two sociolinguistic processes on the same phonological result: increasing application of theLoi de Position. In both communities (one rural and further from Paris, one urban and closer to Paris), there is now no consistent community-wide phonetic distinction between the two phonemes in that environment. It is suggested that theLoi de Positionis already widely applied in the rural site, but speakers are still conscious of the formal norm whereby it is not applied; for the urban site, apparent-time changes for this variable reflect changes in Parisian speech. The theoretical implications of the study concerning speakers’ organisation of their vowel-space, and concerning the increasing application of theLoi de Positionin the French of France, are examined. These conclusions are reached by per-speaker analysis of F1 and F2 separately from each other (rare in French linguistics). As a measure of community cohesion, the article introduces to linguistics the coefficient of variation (more common in biology and medicine).