Five-cluster Model Research Articles

Divisions within the British Parliamentary Labour Party under Keir Starmer: Results of a Cluster Analysis

This paper offers a methodologically innovative two-stage approach for studying divisions amongst parliamentary representatives. Using the Parliamentary Labour Party (PLP) as our case study, we construct a dataset of all Labour MPs elected in the 2019 general election, along with their nominations in the 2020 Labour Party leadership and deputy leadership elections and their membership of, or affiliation with, various party-aligned organisations. We then conduct a cluster analysis based on this dataset, which reveals the existence of a two-cluster model—comprised of the Mainstream (N = 162) and the Left (N = 33)—and a five-cluster model—in which the Left exists alongside the Tribune Soft Left, the Labour Friends of Palestine and the Middle East Soft Left, the Unaligned Centrists, and the Right. Finally, we test the robustness of our clusters via a canonical correspondence analysis of the language used by MPs on social media (Twitter/X) and their contributions to parliamentary debates (Hansard). We show that the MPs from different clusters do use different languages to one another in both fora. We also find that the main divide within the PLP is between the left of the party and the rest of the party, and that the deputy leader, Angela Rayner, has a broader base of support amongst the PLP than its current leader Keir Starmer.

Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques

Urban traffic accidents pose significant challenges to the sustainability of transportation infrastructure not only in Vietnam but also all over the world. To decrease the frequency of accidents, it is crucial to analyze accident data to determine the relationship between accidents and causes, especially for serious accidents. This study suggests an integrated approach using Geographic Information System (GIS) and Data Mining methods to investigate the features of urban traffic accidents in Hanoi, Vietnam aiming to solve these challenges and enhance the safety and efficiency of urban transportation. Firstly, the dataset was segmented into homogenous clusters using the two-step cluster method. Secondly, the correlation between causes and traffic accidents was examined on the overall dataset as well as on each cluster using the association rule mining (ARM) technique. Finally, the location of accident groups and high-frequency sites of accidents (hotspots) were determined by using GIS techniques. As a result, a five-cluster model was created, which corresponded to five common accident groupings in Hanoi. Moreover, the results of the study also identified the types of accidents, the main causes, the time, and the surrounding areas corresponding to each accident group. In detail, cluster 5 depicted accidents on streets, provincial, and national roads caused by motorbikes making up the highest percentage within the groups, accounting for 29.2%. Speeding and driving in the wrong lane in the afternoon and at night were the main causes in this cluster (Cf ≥ 0.9 and Lt ≥ 1.22). Next, cluster 2 had the second-highest proportion. Cluster 2 presented accidents between a truck/car and a motorbike on national and provincial roads, accounting for 27.8%. Cluster 1 presented accidents between a truck/car and a motorbike on local streets, accounting for 22%. Cluster 3 illustrated accidents between two motorbikes on the country lanes, accounting for 12.3%. Finally, cluster 4 depicted single-vehicle motorbike crashes, with the lowest rate of 8.8%. More importantly, this study also recommended using repeatability criteria for the same type of accidents or causes to determine the location of hotspots. Also, suggestions for improving traffic infrastructure sustainability were proposed. To our knowledge, this is the first time in which these three methods are applied simultaneously for analyzing traffic accidents.

Strategies and Tactics to Communicate ‘Nature’: Beyond Anthropocentrism in Swedish Media

Media play a central role in the discursive struggles over the meaning of nature, climate change and human–nature relationships through the strategic selection and salience of media content. This paper investigates the strategies and tactics mobilized by media producers in communicating environmental issues. The investigation is based on a selection of seven media productions in the Swedish context, comprising TV series and documentaries produced between 2015 and 2020. Through a discourse-theoretical analysis of these audio–visual products and interviews with their producers, we identify a five-cluster model, ranging from mainstream anthropocentric strategies to alternative ecocentric tactics. Our model aims to delineate media strategies reproducing hegemonic anthropocentrism to critically inquire about environmental ideologies in media communication and explore potential alternative tactics for more ecocentric representations of nature.

The complexity of theory of mind deficit in schizophrenia: A cross-sectional analysis of baseline data from a longitudinal schizophrenia study

A large number of trials have supported the functional significance of Theory of Mind (ToM) impairment in schizophrenia. However, the nature and the extent of the impairment are still unclear. Reviews on the topic suggest that, in many cases, studies use only one tool to assess the levels of difficulty in the field, limiting the validity of the measurement to one aspect of ToM. On the other hand, the divergence of the used assessment tools makes it hard to compare the result of these studies. Thus, we decided to use additional assessment tools to evaluate the extent of ToM in order to describe several aspects of the phenomenon.A hierarchical cluster analysis of variables was used on a sample of 68 participants with schizophrenia or schizoaffective disorder, to determine the similarity between variances of the assessed ToM subcomponents. Further cross-sectional correlational analysis was then performed to investigate the association between the identified clusters and other used measures (e.g.: neurocognition).The statistical analysis supported a five-cluster model. Identified clusters illustrate the difference between Hypo and HyperToM as well as the degree of ToM task complexity, allowing for a more accurate description of the nature of ToM deficit in schizophrenia.

The lowest excited states of 14C and 14O nuclei within a five-cluster model

We study the ground and the first excited 0+ states of two mirror nuclei 14C and 14O within a five cluster model (three alpha-particles and two extra nucleons) with the use of high accuracy variational approach with Gaussian bases. The first excited 0+ state of these nuclei is shown to be connected with a change of the structure of the two nucleon subsystem moving in the field of the 12C cluster. The density distributions, electric form factors (both the elastic and transition ones), pair correlation functions, and the momentum distributions of the constituent particles are found. The most probable shapes of five-cluster systems are revealed.

An Empirical Test of Dual System Pathways

Although a growing body of research has documented the characteristics and experiences of young people with child welfare and juvenile justice system contact (i.e., dual system involvement), less is known about the pathways leading to dual system involvement. Sequence analysis was used to empirically identify pathways by plotting child welfare and juvenile justice spells for a cohort of 4404 dual system youth in Los Angeles County between 2014 to 2016. Procedures produced a five-cluster model delineated by the extent of child welfare involvement and the ages at which involvement occurred. Comparisons across pathways demonstrated that youth with less child welfare involvement in childhood had the least intrusive juvenile justice experiences and lower recidivism than youth experiencing longer and deeper child welfare involvement in late childhood and adolescence. Pathway findings have significant implications for building a delinquency prevention continuum prior to system involvement and after involvement for young people and their family members who enter the child welfare system.

Exploring sensory phenotypes in autism spectrum disorder

BackgroundAtypical reactions to the sensory environment are often reported in autistic individuals, with a high degree of variability across the sensory modalities. These sensory differences have been shown to promote challenging behaviours and distress in autistic individuals and are predictive of other functions including motor, social, and cognitive abilities. Preliminary research suggests that specific sensory differences may cluster together within individuals creating discrete sensory phenotypes. However, the manner in which these sensory differences cluster, and whether the resulting phenotypes are associated with specific cognitive and social challenges is unclear.MethodsShort sensory profile data from 599 autistic children and adults between the ages of 1 and 21 years were subjected to a K-means cluster analysis. Analysis of variances compared age, adaptive behaviour, and traits associated with autism, attention-deficit and hyperactivity disorder, and obsessive and compulsive disorder across the resultant clusters.ResultsA five-cluster model was found to minimize error variance and produce five sensory phenotypes: (1) sensory adaptive, (2) generalized sensory differences, (3) taste and smell sensitivity, (4) under-responsive and sensation seeking, and (5) movement difficulties with low energy. Age, adaptive behaviour, and traits associated with autism, attention-deficit and hyperactivity disorder, and obsessive and compulsive disorder were found to differ significantly across the five phenotypes.LimitationsThe results were based on parent-report measures of sensory processing, adaptive behaviour, traits associated with autism, attention-deficit and hyperactivity disorder, and obsessive and compulsive disorder, which may limit the generalizability of the findings. Further, not all measures are standardized, or psychometrically validated with an autism population. Autistic individuals with an intellectual disability were underrepresented in this sample. Further, as these data were obtained from established records from a large provincial database, not all measures were completed for all individuals.ConclusionsThese findings suggest that sensory difficulties in autistic individuals can be clustered into sensory phenotypes, and that these phenotypes are associated with behavioural differences. Given the large degree of heterogeneity in sensory difficulties seen in the autistic population, these sensory phenotypes represent an effective way to parse that heterogeneity and create phenotypes that may aid in the development of effective treatments and interventions for sensory difficulties.

Developing a model of citizenship for application within health and social care contexts: a community-based participatory approach

BackgroundCitizenship as a concept has often been understood in terms of the duties, rights, obligations, and functions a person has as a member of society. In health and social care policy and practice, the term has broader reach. This study explored what citizenship means to people with recent experience of mental illness, a long-term physical health condition, or involvement with the criminal justice, as well as to people who did not primarily identify as having experienced any of these major life disruptions. The aim was to develop an empirical model of citizenship to be applied within health and social care contexts. MethodsWe employed a mixed-methods, community-based, participatory research approach, with recruitment taking place between June, 2016, and February, 2019. Ten focus groups (n=77 participants) were conducted to generate statement items about the meaning of citizenship, and concept-mapping sessions with participants from the stakeholder groups (n=45) were held to categorise and rate each item in terms of importance and achievement. The number of statement items generated was further reduced after an online survey to ask participants (n=242) to rate items according to their clarity and relevance to the concept of citizenship. Multidimensional scaling and hierarchical cluster analysis were used to develop a five-cluster model of citizenship. Findings110 statement items about the meaning of citizenship were generated from a total of 703 items extracted from the focus groups, and were further reduced to 58 following the online survey. We identified five clusters representing the personal meanings of citizenship for participants: building relationships, autonomy and acceptance, access to services and supports, values and social roles, and civic rights and responsibilities. These clusters informed the development of the empirical model of citizenship to be applied within health and social care policy and practice. InterpretationThis multidimensional model of citizenship provides an empirical framework for policy makers and practitioners to set citizenship-based initiatives that contribute to the recovery and social inclusion of people who have experienced major life disruptions. This model will inform the development of a citizenship tool to facilitate discussions around citizenship. FundingTurning Point Scotland, The Scottish Recovery Network and Health and Social Care Alliance Scotland.

Subtyping psychological distress in the population: a semi-parametric network approach.

The mechanisms underlying both depressive and anxiety disorders remain poorly understood. One of the reasons for this is the lack of a valid, evidence-based system to classify persons into specific subtypes based on their depressive and/or anxiety symptomatology. In order to do this without a priori assumptions, non-parametric statistical methods seem the optimal choice. Moreover, to define subtypes according to their symptom profiles and inter-relations between symptoms, network models may be very useful. This study aimed to evaluate the potential usefulness of this approach. A large community sample from the Canadian general population (N = 254 443) was divided into data-driven clusters using non-parametric k-means clustering. Participants were clustered according to their (co)variation around the grand mean on each item of the Kessler Psychological Distress Scale (K10). Next, to evaluate cluster differences, semi-parametric network models were fitted in each cluster and node centrality indices and network density measures were compared. A five-cluster model was obtained from the cluster analyses. Network density varied across clusters, and was highest for the cluster of people with the lowest K10 severity ratings. In three cluster networks, depressive symptoms (e.g. feeling depressed, restless, hopeless) had the highest centrality. In the remaining two clusters, symptom networks were characterised by a higher prominence of somatic symptoms (e.g. restlessness, nervousness). Finding data-driven subtypes based on psychological distress using non-parametric methods can be a fruitful approach, yielding clusters of persons that differ in illness severity as well as in the structure and strengths of inter-symptom relationships.

Structure of 14N Nucleus within a Five-Cluster Model

The spatial structure of 14N nucleus is studied within a five-particle model (three a-particles plus two nucleons). Using the variational approach with Gaussian bases, the ground-state energy and wave function are calculated for this five-particle system. Two spatial configurations in the ground-state wave function are revealed. The density distributions, pair correlation functions, and the momentum distributions of particles are analyzed and compared with those of the mirror nuclei 14C and 14O.

Towards global music digital libraries

Purpose The purpose of this paper is to compare music mood perceptions of people with diverse cultural backgrounds when they interact with Chinese music. It also discusses how the results can inform the design of global music digital libraries (MDL). Design/methodology/approach An online survey was designed based on the Music Information Retrieval Evaluation eXchange (MIREX) five-cluster mood model, to solicit mood perceptions of listeners in Hong Kong and the USA on a diverse set of Chinese music. Statistical analysis was applied to compare responses from the two user groups, with consideration of different music types and characteristics of listeners. Listeners’ textual responses were also analyzed with content coding. Findings Listeners from the two cultural groups made different mood judgments on all but one type of Chinese music. Hong Kong listeners reached higher levels of agreement on mood judgments than their US counterparts. Gender, age and familiarity with the songs were related to listeners’ mood judgment to some extent. Practical implications The MIREX five-cluster model may not be sufficient for representing the mood of Chinese music. Refinements are suggested. MDL are recommended to differentiate tags given by users from different cultural groups, and to differentiate music types when classifying or recommending Chinese music by mood. Originality/value It is the first study on cross-cultural access to Chinese music in MDL. Methods and the refined mood model can be applied to cross-cultural access to other music types and information objects.

Cognitive disorders in Parkinson's disease: Confirmation of a spectrum of severity.

Clinical presentation and progression of cognitive disorders in Parkinson's disease (PD) is heterogeneous. Our objective was to confirm prospectively a previous exploratory cluster analysis based on retrospective data that identified five cognitive phenotypes in PD. A model-based confirmatory cluster analysis was conducted on the results of neuropsychological tests administered in 156 PD patients from two European movement disorder centers (Lille, n=81; Maastricht, n=75). The number of clusters was determined on the basis of statistical criteria as well as clinical plausibility. A factorial discriminant analysis assessed the quality of the clusters' separation. A five-cluster model was statistically superior and clinically the most relevant. These clusters can be described as follows: 1) cognitively intact patients with high level of performance in all cognitive domains (25.64%), 2) cognitively intact patients slightly slower than those in cluster 1 (26.92%), 3) patients with deficits in executive functions (37.18%), 4) patients with severe deficits in all cognitive domains, particularly executive functions (3.20%), 5) patients with severe deficits in all cognitive domains, particularly working memory and recall in verbal episodic memory (7.05%). The groups differed in terms of age, apathy and frequency of hallucinations that were all higher in the clusters with cognitive deficits, and the duration of formal education was lower in those groups. We confirm our previous exploratory analysis. Cognitive disorders in PD patients are heterogeneous and can be separated in five clusters ranging from patients with performance in the normal range to patients with severe disorders in all cognitive domains.

Measuring stages of recovery from psychosis

BackgroundMental health consumers invite us to abandon the pathology model, which is tied to pessimism, and instead to embrace a model of personal recovery that goes beyond being free from symptoms, and involves self-management of the illness. The Stages of Recovery Instrument (STORI) is a measure developed from the perspective of consumers according to a conceptual five-stage model of recovery. AimsThe main aim of this work was to study the psychometric properties of the STORI, but we also set out to compare the stages of recovery in our sample with the five-stage model in the sample with which the scale was developed. MethodsOur sample consisted of 95 people diagnosed with schizophrenia-spectrum psychoses, with a mean age of 34.74 (SD=9.25). ResultsThe STORI scores showed adequate psychometric properties in this sample. Cluster analysis indicated that the three-cluster model fitted the data better than the five-cluster model. Internal consistency of the STORI scores ranged between .83 and .87. STORI stages were associated with Recovery Styles Questionnaire scores. DiscussionThe results provide empirical validation of the STORI in other countries. Empirical evidence revealed that the stages of recovery found in our own and other clinical samples differ from those found in the samples with which the scale was developed.

Psychological profiling of sexual murders: an empirical model.

Psychological profiling represents the investigative technique of analyzing crime behaviors for the identification of probable offender characteristics. Profiling has progressively been incorporated into police procedures despite a surprising lack of empirical research to support its validity. Indeed, in the study of sexual murder for the purpose of profiling, very few quantitative, academically reviewed studies exist. This article reports on the results of a 4-year study into Australian sexual murders for the development of psychological profiling. The study involved 85 cases of sexual murder sampled from all Australian police jurisdictions. The statistical procedure of multidimensional scaling was employed. This analysis produced a five-cluster model of sexual murder behavior. First, a central cluster of behaviors was identified that represents common behaviors to all patterns of sexual murder. Next, four distinct outlying patterns--predator, fury, perversion, and rape--were identified that each demonstrated distinct offense styles. Further analysis of these patterns also identified distinct offender characteristics that allow for the use of empirically robust offender profiles in future sexual murder investigations.

Psychological profiling of offender characteristics from crime behaviors in serial rape offences.

Criminal psychological profiling has progressively been incorporated into police procedures despite a dearth of empirical research. Indeed, in the study of serial violent crimes for the purpose of psychological profiling, very few original, quantitative, academically reviewed studies actually exist. This article reports on the analysis of 62 incidents of serial sexual assault. The statistical procedure of multidimensional scaling was employed in the analysis of this data, which in turn produced a five-cluster model of serial rapist behavior. First, a central cluster of behaviors were identified that represent common behaviors to all patterns of serial rape. Second, four distinct outlying patterns were identified as demonstrating distinct offence styles, these being assigned the following descriptive labels brutality, intercourse, chaotic, and ritual. Furthermore, analysis of these patterns also identified distinct offender characteristics that allow for the use of empirically robust offender profiles in future serial rape investigations.