scholarly journals Item and domain network structures of the Resilience Scale for Adults in 675 university students

2019 ◽  
Author(s):  
Giovanni Briganti ◽  
Paul Linkowski
Keyword(s):  
Network Analysis ◽  
Social Competence ◽  
University Students ◽  
Protective Factors ◽  
Network Structure ◽  
Family Cohesion ◽  
Clinical Intervention ◽  
Resilience Scale ◽  
French Speaking ◽  
Original Number

Aims: The Resilience Scale for Adults (RSA) is a questionnaire that measures protective factors of mental health. The aim of this paper is to perform a network analysis of the Resilience Scale for Adults (RSA) in a dataset composed of 675 French-speaking Belgian university students, to identify potential targets for intervention to improve protective factors in individuals.Methods: We estimated a network structure for the 33-item questionnaire and for the six domains of resilience: perception of self, planned future, social competence, structured style, family cohesion and social competence. Node predictability (shared variance with surrounding nodes in the network) was used to assess the connectivity of items. An Exploratory Graph Analysis (EGA) was performed to detect communities in the network: the number of communities detected being different than the original number of factors proposed in the scale, we estimated a new network with the resulting structure and verified the validity of the new construct which was proposed. We provide the anonymized dataset and code in the supplementary materials to ensure complete reproducibility of the results.Results: The network composed of items from the RSA is overall positively connected with strongest connections arising among items from the same domain. The domain network reports several connections, both positive and negative. The EGA reported the existence of four communities that we propose as an additional network structure. Node predictability estimates show that connectedness varies among the items and domains of the RSA.Conclusions: Network analysis is a useful tool to explore resilience and identify targets for clinical intervention. In this study, the four domains acting as components of the additional four-domain network structure may be potential targets to improve an individual’s resilience. Further studies may endeavor to replicate our findings in different samples.

scholarly journals Item and domain network structures of the Resilience Scale for Adults in 675 university students

2019 ◽  
Vol 29 ◽  
Author(s):  
G. Briganti ◽  
P. Linkowski
Keyword(s):  
Network Analysis ◽  
Social Competence ◽  
University Students ◽  
Protective Factors ◽  
Network Structure ◽  
Family Cohesion ◽  
Clinical Intervention ◽  
Resilience Scale ◽  
French Speaking ◽  
Original Number

Abstract Aims The Resilience Scale for Adults (RSA) is a questionnaire that measures protective factors of mental health. The aim of this paper is to perform a network analysis of the RSA in a dataset composed of 675 French-speaking Belgian university students, to identify potential targets for intervention to improve protective factors in individuals. Methods We estimated a network structure for the 33-item questionnaire and for the six domains of resilience: perception of self, planned future, social competence, structured style, family cohesion and social competence. Node predictability (shared variance with surrounding nodes in the network) was used to assess the connectivity of items. An exploratory graph analysis (EGA) was performed to detect communities in the network: the number of communities detected being different than the original number of factors proposed in the scale, we estimated a new network with the resulting structure and verified the validity of the new construct which was proposed. We provide the anonymised dataset and code in external online materials (10.17632/64db36w8kf.2) to ensure complete reproducibility of the results. Results The network composed of items from the RSA is overall positively connected with strongest connections arising among items from the same domain. The domain network reports several connections, both positive and negative. The EGA reported the existence of four communities that we propose as an additional network structure. Node predictability estimates show that connectedness varies among the items and domains of the RSA. Conclusions Network analysis is a useful tool to explore resilience and identify targets for clinical intervention. In this study, the four domains acting as components of the additional four-domain network structure may be potential targets to improve an individual's resilience. Further studies may endeavour to replicate our findings in different samples.

scholarly journals Exploring network structure and central items of the Narcissistic Personality Inventory

2019 ◽  
Author(s):  
Giovanni Briganti ◽  
Paul Linkowski
Keyword(s):  
Ising Model ◽  
Network Analysis ◽  
University Students ◽  
Network Structure ◽  
Personality Inventory ◽  
Future Studies ◽  
Connected Network ◽  
Narcissistic Personality ◽  
Narcissistic Personality Inventory ◽  
French Speaking

Objectives: The aim of this work is to explore the Narcissistic Personality Inventory (NPI) using network analysis in a dataset of 942 university students from the French-speaking part of Belgium.Methods: We estimated an Ising Model for the forty items in the questionnaire and explored item interconnectedness with strength centrality. We provide in the supplementary materials the dataset used for the analyses as well as the full code to ensure the reproducibility of our results.Results: The NPI is presented as an overall positively connected network with items from entitlement, authority and superiority reporting the highest centrality estimates.Conclusions: Network analysis highlights new properties of items from the NPI. Future studies should endeavor to replicate our findings in other samples, both clinical and non-clinical.

scholarly journals The item network and domain network of burnout in Chinese nurses

BMC Nursing ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Lin Wu ◽  
Lei Ren ◽  
Yifei Wang ◽  
Kan Zhang ◽  
Peng Fang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Community Detection ◽  
Emotional Exhaustion ◽  
Network Structure ◽  
Partial Correlation ◽  
Clinical Intervention ◽  
Maslach Burnout Inventory ◽  
Cluster Sampling ◽  
Occupational Burnout ◽  
Nurse Burnout

Abstract Background As a common social phenomenon, nurses’ occupational burnout has a high incidence rate, which seriously affects their mental health and nursing level. The current assessment mostly uses the total score model and explores the influence of external factors on burnout, while the correlation between burnout items or dimensions is less explored. Ignoring the correlation between the items or dimensions may result in a limited understanding of nurse occupational burnout. This paper explores the item and dimension network structure of the Maslach Burnout Inventory-General Survey (MBI-GS) in Chinese nurses, so as to gain a deeper understanding of this psychological construct and identify potential targets for clinical intervention. Methods A total of 493 Chinese nurses were recruited by cluster sampling. All participants were invited to complete the survey on symptoms of burnout. Network analysis was used to investigate the item network of MBI-GS. In addition, community detection was used to explore the communities of MBI-GS, and then network analysis was used to investigate the dimension network of MBI-GS based on the results of community detection. Regularized partial correlation and non-regularized partial correlation were used to describe the association between different nodes of the item network and dimension network, respectively. Expected influence and predictability were used to describe the relative importance and the controllability of nodes in both the item and dimension networks. Results In the item network, most of the strongly correlated edges were in the same dimension of emotional exhaustion (E), cynicism (C) and reduced professional efficacy (R), respectively. E5 (Item 5 of emotional exhaustion, the same below) “I feel burned out from my work”, C1 “I have become more callous toward work since I took this job”, and R3 “In my opinion, I am good at my job” had the highest expected influence (z-scores = 0.99, 0.81 and 0.94, respectively), indicating theirs highest importance in the network. E1 “I feel emotionally drained from my work” and E5 had the highest predictability (E1 = 0.74, E5 = 0.74). It shows that these two nodes can be interpreted by their internal neighbors to the greatest extent and have the highest controllability in the network. The spinglass algorithm and walktrap algorithm obtained exactly the same three communities, which are consistent with the original dimensions of MBI-GS. In the dimension network, the emotional exhaustion dimension was closely related to the cynicism dimension (weight = 0.65). Conclusions The network model is a useful tool to study burnout in Chinese nurses. This study explores the item and domain network structure of nurse burnout from the network perspective. By calculating the relevant indicators, we found that E5, C1, and R3 were the most central nodes in the item network and cynicism was the central node in the domain network, suggesting that interventions aimed at E5, C1, R3 and cynicism might decrease the overall burnout level of Chinese nurses to the greatest extent. This study provides potential targets and a new way of thinking for the intervention of nurse burnout, which can be explored and verified in clinical practice.

scholarly journals Network Structures of Symptoms From the Zung Depression Scale

2020 ◽  
pp. 003329412094211
Author(s):  
Giovanni Briganti ◽  
Marco Scutari ◽  
Paul Linkowski
Keyword(s):  
Network Analysis ◽  
Depression Scale ◽  
Clinical Intervention ◽  
Male Students ◽  
Clinical Samples ◽  
Depression Symptoms ◽  
Network Structures ◽  
French Speaking ◽  
Node Connectivity ◽  
Zung Depression Scale

The Self-rating Depression Scale (SDS) is a psychometric tool composed of 20 items used to assess depression symptoms. The aim of this work is to perform a network analysis of this scale in a large sample composed of 1090 French-speaking Belgian university students. We estimated a regularized partial correlation network and a Directed Acyclic Graph for the 20 items of the questionnaire. Node predictability (shared variance with surrounding nodes in the network) was used to assess the connectivity of items. The network comparison test was performed to compare networks from female and male students. The network composed of items from the SDS is overall positively connected, although node connectivity varies. Item 11 (“My mind is as clear as it used to be”) is the most interconnected item. Networks from female and male students did not differ. DAG reported directed edges among items. Network analysis is a useful tool to explore depression symptoms and offers new insight as to how they interact. Further studies may endeavor to replicate our findings in different samples, including clinical samples to replicate the network structures and determine possible viable targets for clinical intervention.

scholarly journals Network structures of symptoms from the Zung Depression Scale

2020 ◽  
Author(s):  
Giovanni Briganti ◽  
Marco Scutari ◽  
Paul Linkowski
Keyword(s):  
Network Analysis ◽  
Depression Scale ◽  
Clinical Intervention ◽  
Male Students ◽  
Clinical Samples ◽  
Depression Symptoms ◽  
Network Structures ◽  
French Speaking ◽  
Node Connectivity ◽  
Zung Depression Scale

The Self-rating Depression Scale (SDS) is a psychometric tool composed of 20 items used to assess depression symptoms. The aim of this work is to perform a network analysis of this scale in a large sample composed of 1090 French-speaking Belgian university students. We estimated a regularized partial correlation network and a Directed Acyclic Graph for the 20 items of the questionnaire. Node predictability (shared variance with surrounding nodes in the network) was used to assess the connectivity of items. The network comparison test was performed to compare networks from female and male students. The network composed of items from the SDS is overall positively connected, although node connectivity varies. Item 11 (“My mind is as clear as it used to be”) is the most interconnected item. Networks from female and male students did not differ. DAG reported directed edges among items. Network analysis is a useful tool to explore depression symptoms and offers new insight as to how they interact. Further studies may endeavor to replicate our findings in different samples, including clinical samples to replicate the network structures and determine possible viable targets for clinical intervention.

Resilience of Parents of Young University Students with Disabilities

2018 ◽  
Vol 5 (2) ◽  
pp. 137
Author(s):  
Eleticia Isabel Pinargote Macías ◽  
Francisco Ashley Gavilanes Vaca ◽  
Víctor Hugo Cedeño Gavilánez
Keyword(s):  
University Students ◽  
Students With Disabilities ◽  
Representative Sample ◽  
Conceptual Analysis ◽  
Decisive Role ◽  
Young Person ◽  
Resilience Scale ◽  
The Family ◽  
Development Of Students ◽  
Family Dimension

The resilience of parents can play a decisive role as a resource that favors the inclusion and development of students with disabilities, representing a decisive contribution in school-family co-responsibility. This work showed a conceptual analysis related to resilience from a family dimension and especially the role played by parents. The research was carried out in the context of the Technical University of Manabí, a representative sample of students with disabilities and their families was selected, two instruments were applied to obtain the data: Family Functioning Scale [1] and the Mother Resilience Scale [2]. The attention to the young person with a disability was analyzed, and it is particularized in the related to the family of these. The results are shown in tables that allow the final results to be identified.

scholarly journals Bayesian Network Structure and Predictability of Autistic Traits

2020 ◽  
pp. 003329412097815
Author(s):  
Giovanni Briganti ◽  
Donald R. Williams ◽  
Joris Mulder ◽  
Paul Linkowski
Keyword(s):  
Sex Differences ◽  
Network Analysis ◽  
Bayesian Network ◽  
Network Structure ◽  
Network Connectivity ◽  
Autistic Traits ◽  
Data Set ◽  
Conditional Dependence ◽  
Bayesian Network Structure ◽  
Male Subjects

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.

scholarly journals Tight knit under stress: colony resilience to the loss of tandem leaders during relocation in an Indian ant

2015 ◽  
Vol 2 (9) ◽  
pp. 150104 ◽  
Author(s):  
Swetashree Kolay ◽  
Sumana Annagiri
Keyword(s):  
Network Analysis ◽  
Social Insects ◽  
Network Structure ◽  
Similar Rate ◽  
Tandem Running ◽  
Frequent Event ◽  
Random Removal ◽  
Task Organization

The movement of colonies from one nest to another is a frequent event in the lives of many social insects and is important for their survival and propagation. This goal-oriented task is accomplished by means of tandem running in some ant species, such as Diacamma indicum . Tandem leaders are central to this process as they know the location of the new nest and lead colony members to it. Relocations involving targeted removal of leaders were compared with unmanipulated and random member removal relocations. Behavioural observations were integrated with network analysis to examine the differences in the pattern of task organization at the level of individuals and that of the colony. All colonies completed relocation successfully and leaders who substituted the removed tandem leaders conducted the task at a similar rate having redistributed the task in a less skewed manner. In terms of network structure, this resilience was due to significantly higher density and outcloseness indicating increased interaction between substitute leaders. By contrast, leader–follower interactions and random removal networks showed no discernible changes. Similar explorations of other goal-oriented tasks in other societies will possibly unveil new facets in the interplay between individuals that enable the group to respond effectively to stress.

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