Examining novice anaesthesia trainee simulation performance: a tale of two clusters

2021 ◽  
pp. bmjstel-2020-000812
Author(s):  
Rodrigo J Daly Guris ◽  
Christina R Miller ◽  
Adam Schiavi ◽  
Serkan Toy
Keyword(s):  
Cluster Analysis ◽  
Pilot Study ◽  
Technical Skill ◽  
Cluster Solution ◽  
Simulation Studies ◽  
Speaking Up ◽  
Cluster Membership ◽  
Task Load ◽  
Simulation Performance ◽  
Performance Differences

IntroductionUnderstanding performance differences between learners may provide useful context for optimising medical education. This pilot study aimed to explore a technique to contextualise performance differences through retrospective secondary analyses of two randomised controlled simulation studies. One study focused on speaking up (non-technical skill); the other focused on oxygen desaturation management (technical skill).MethodsWe retrospectively analysed data from two independent simulation studies conducted in 2017 and 2018. We used multivariate hierarchical cluster analysis to explore whether participants in each study formed homogenous performance clusters. We then used mixed-design analyses of variance and χ2 analyses to examine whether reported task load differences or demographic variables were associated with cluster membership.ResultsIn both instances, a two-cluster solution emerged; one cluster represented trainees exhibiting higher performance relative to peers in the second cluster. Cluster membership was independent of experimental allocation in each of the original studies. There were no discernible demographic differences between cluster members. Performance differences between clusters persisted for at least 8 months for the non-technical skill but quickly disappeared following simulation training for the technical skill. High performers in speaking up initially reported lower task load than standard performers, a difference that disappeared over time. There was no association between performance and task load during desaturation management.ConclusionThis pilot study suggests that cluster analysis can be used to objectively identify high-performing trainees for both a technical and a non-technical skill as observed in a simulated clinical setting. Non-technical skills may be more difficult to teach and retain than purely technical ones, and there may be an association between task load and initial non-technical performance. Further study is needed to understand what factors may confer inherent performance advantages, whether these advantages translate to clinical performance and how curricula can best be designed to drive targeted improvement for individual trainees.

scholarly journals Sleep and cardiometabolic risk: a cluster analysis of actigraphy-derived sleep profiles in adults and children

SLEEP ◽  
2021 ◽  
Author(s):  
Lisa Matricciani ◽  
Catherine Paquet ◽  
François Fraysse ◽  
Anneke Grobler ◽  
Yichao Wang ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Cardiometabolic Risk ◽  
High Efficiency ◽  
Cardiometabolic Health ◽  
Maturity Stage ◽  
Cluster Membership ◽  
Geographic Clustering ◽  
Adults And Children ◽  
Study Participants ◽  
Generalised Estimating Equations

Abstract Study objectives Sleep plays an important role in cardiometabolic health. While the importance of considering sleep as a multidimensional construct is widely appreciated, studies have largely focused on individual sleep characteristics. The association between actigraphy-derived sleep profiles and cardiometabolic health in healthy adults and children has not been examined. Methods This study used actigraphy-measured sleep data collected between February 2015 and March 2016 in the Child Health CheckPoint study. Participants wore actigraphy monitors (GENEActiv Original, Cambs, UK) on their non-dominant wrist for seven days and sleep characteristics (period, efficiency, timing and variability) were derived from raw actigraphy data. Actigraphy-derived sleep profiles of 1,043 Australian children aged 11-12 years and 1337 adults were determined using K-means cluster analysis. The association between cluster membership and biomarkers of cardiometabolic health (blood pressure, body mass index, apolipoproteins, glycoprotein acetyls, composite metabolic syndrome severity score) were assessed using Generalised Estimating Equations, adjusting for geographic clustering, with sex, socioeconomic status, maturity stage (age for adults, pubertal status for children) and season of data collection as covariates. Results Four actigraphy-derived sleep profiles were identified in both children and adults: Short sleepers, Late to bed, Long sleepers, and Overall good sleepers. The Overall good sleeper pattern (characterised by adequate sleep period time, high efficiency, early bedtime and low day-to-day variability) was associated with better cardiometabolic health in the majority of comparisons (80%). Conclusion Actigraphy-derived sleep profiles are associated with cardiometabolic health in adults and children. The Overall good sleeper pattern is associated with more favourable cardiometabolic health.

scholarly journals Heart and Mind: Essential Components in Sound Decision-Making

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 364-364
Author(s):  
Michaela Clark ◽  
Julie Hicks Patrick ◽  
Michaela Reardon
Keyword(s):  
Decision Making ◽  
Decision Quality ◽  
Cluster Solution ◽  
Cognitive Resources ◽  
Cluster Membership ◽  
Essential Components ◽  
Decision Making Processes ◽  
Affective Components ◽  
Heart And Mind

Abstract Consumer tasks permit an ecologically-valid context in which to examine the contributions of affective and cognitive resources to decision-making processes and outcomes. Although previous work shows that cognitive factors are important when individuals make decisions (Patrick et al., 2013; Queen et al.), the role of affective components is less clear. We examine these issues in two studies. Study 1 used data from 1000+ adults to inform a cluster analysis examining affective aspects (importance, meaningfulness) of making different types of decisions. A 4-cluster solution resulted. In Study 2, we used affective cluster membership and cognitive performance as predictors of experimental decision-making outcomes among a subset of participants (N = 60). Results of the regression (F(2, 40) = 6.51, p < .01, R2 = .25.) revealed that both the affective clusters (b = .37, p = .01) and cognitive ability (b = -.30, p = .04) uniquely contributed to the variance explained in decision quality. Age did not uniquely contribute. Results are discussed in the context of developing measures that enable us to move the field forward.

scholarly journals An Analysis of Heterogeneity in German Speaking Birdwatchers Reveals Three Distinct Clusters and Gender Differences

Birds ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 250-260
Author(s):  
Christoph Randler
Keyword(s):  
Cluster Analysis ◽  
Optimal Solution ◽  
Social Bonding ◽  
Cluster Solution ◽  
Novice Group ◽  
Wide Range ◽  
German Speaking ◽  
Group Members ◽  
Bird Group ◽  
And Gender

The purpose of this study was to segment birdwatchers into clusters. Members from a wide range of bird related organizations, from highly specialized birders as well as Facebook bird group members were studied to provide a diverse dataset (n = 2766; 50.5% men). Birding specialization was measured with a battery of questionnaires. Birding specialization encompassed the three constructs of skill/competence, behavior, personal and behavioral commitment. Additionally, involvement, measured by centrality to lifestyle, attraction, social bonding, and identity, was used. The NbClust analyses showed that a three-cluster solution was the optimal solution. Then, k-means cluster analysis was applied on three groups: casual/novice, intermediate, and specialist/advanced birdwatchers. More men than women were in the specialist/advanced group and more women than men in the casual/novice group. As a conclusion, this study confirms a three-cluster solution for segmenting German birdwatchers based on a large and diverse sample and a broad conceptualization of the construct birding specialization. These data can be used to address different target audiences (novices, advanced birders) with different programs, e.g., in nature conservation.

A taxonomy of the scope and organization of local sexually transmitted disease services for policy and practice

2018 ◽  
Vol 29 (14) ◽  
pp. 1375-1383
Author(s):  
Hector P Rodriguez ◽  
Summer Starling ◽  
Zosha Kandel ◽  
Robert Weech-Maldonado ◽  
Nicholas J Moss ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Critical Role ◽  
Transmitted Disease ◽  
The United States ◽  
Incidence Rates ◽  
Contact Tracing ◽  
Cluster Solution ◽  
Local Health ◽  
Sexually Transmitted

Local health departments (LHDs) and their organizational partners play a critical role in controlling sexually transmitted diseases (STDs) in the United States. We examine variation in the differentiation, integration, and concentration (DIC) of STD services and develop a taxonomy describing the scope and organization of local STD services. LHD STD programs (n = 115) in Alabama (AL) and California (CA) responded to surveys assessing STD services available in 2014. K-means cluster analysis identified LHD groupings based on DIC variation. Discriminant analysis validated cluster solutions. Differences in organizational partnerships and scope of STD services were compared by taxonomy category. Multivariable regression models estimated the association of the STD services organization taxonomy and five-year (2010–2014) gonorrhea incidence rates, controlling for county-level sociodemographics and resources. A three-cluster solution was identified: (1) low DIC (n = 74), (2) moderate DIC (n = 31), and (3) high DIC (n = 10). In discriminant analysis, 95% of jurisdictions were classified into the same types as originally assigned through K-means cluster analysis. High DIC jurisdictions were more likely (p < 0.001) to partner with most organizations than moderate and low DIC jurisdictions, and more likely (p < 0.001) to conduct STD needs assessment, comprehensive sex education, and targeted screening. In contrast, contact tracing, case management, and investigations were conducted similarly across jurisdictions. In adjusted analyses, there were no differences in gonorrhea incidence rates by category. Jurisdictions in CA and AL can be characterized into three distinct clusters based on the DIC of STD services. Taxonomic analyses may aid in improving the reach and effectiveness of STD services.

scholarly journals Using Clustering as a Tool: Mixed Methods in Qualitative Data Analysis

2015 ◽  
Author(s):  
Laura Macia
Keyword(s):  
Cluster Analysis ◽  
Mixed Methods ◽  
Data Analysis ◽  
Qualitative Data ◽  
Similarity Measures ◽  
Cluster Solution ◽  
Clustering Methods ◽  
Qualitative Data Analysis ◽  
Research Project ◽  
Detailed Explanation

In this article I discuss cluster analysis as an exploratory tool to support the identification of associations within qualitative data. While not appropriate for all qualitative projects, cluster analysis can be particularly helpful in identifying patterns where numerous cases are studied. I use as illustration a research project on Latino grievances to offer a detailed explanation of the main steps in cluster analysis, providing specific considerations for its use with qualitative data. I specifically describe the issues of data transformation, the choice of clustering methods and similarity measures, the identification of a cluster solution, and the interpretation of the data in a qualitative context.

The Transtheoretical Model in Intimate Partner Violence Victimization: Stage Changes Over Time

2008 ◽  
Vol 23 (4) ◽  
pp. 411-431 ◽  
Author(s):  
Kelly H. Burkitt ◽  
Gregory L. Larkin
Keyword(s):  
Intimate Partner Violence ◽  
Transtheoretical Model ◽  
Partner Violence ◽  
Intimate Partner ◽  
Cluster Solution ◽  
Community Resources ◽  
Process Of Change ◽  
Cluster Membership ◽  
Changes Over Time ◽  
Over Time

The transtheoretical model of behavior change (TTM) has been extended to describe the process of change in victims of intimate partner violence (IPV); however, it has not been validated over time or in a population of women experiencing IPV who are not currently in shelter. This article examines the process of change in IPV victims longitudinally and identifies factors that may relate to staging and stage progression. Fifty-three women were enrolled on presentation to an emergency department for health care treatment and completed follow-up at 3 to 4 months. Measures of TTM staging, use of community resources, ongoing abuse, mental health, and social support were collected. Cluster analyses were conducted, and descriptive summaries of clusters and significant demographic, abuse, and outcome variables related to cluster membership are presented. A five-cluster solution was selected on the basis of parsimony, theory, and overall coherence with the data. Forward progression through the stages over time was related to both the use of community resources and ending the IPV relationship.

A Model for Make-or-Buy Decisions in Engineering Design Services Sector: A Case Study from Turkey

2018 ◽  
Vol 15 (04) ◽  
pp. 1850038
Author(s):  
Z. Aytan Ediz ◽  
M. Atilla Öner ◽  
Y. Can Erdem ◽  
Nesimi Kaplan
Keyword(s):  
Decision Making ◽  
Cluster Analysis ◽  
Engineering Design ◽  
Administrative Support ◽  
Social Dynamics ◽  
Successful Implementation ◽  
Cluster Membership ◽  
Services Sector ◽  
Engineering Company

Make-or-buy decision is an important factor affecting the profitability of the firms in all sectors. The goal of this study is to propose a model for firms in engineering design services sector for make-or-buy decisions. A survey was conducted to determine the importance percentages given in an engineering company in make-or-buy decisions and a model was developed. The results of the case study show intriguing clusters of company personnel. As the lack of consensus among company managers and personnel may inhibit the successful implementation of the developed strategy, we use K-Means Clustering to determine the different perspectives of different groups of employees (managers, senior engineers, junior engineers, technical and administrative support personnel) which may contribute to the understanding of social dynamics of decision making within the company. 4-cluster and 5-cluster analysis results indicate the need for further study on the dynamics of cluster membership.

“Money Worlds” and Wellbeing

2018 ◽  
pp. 74-101
Author(s):  
Leisa Reinecke Flynn ◽  
Ronald Earl Goldsmith ◽  
Michael Brusco
Keyword(s):  
Cluster Analysis ◽  
Consumer Culture ◽  
The Other ◽  
Price Sensitivity ◽  
Cluster Solution ◽  
Status Consumption ◽  
Brand Engagement ◽  
Attitudes And Behaviors ◽  
And Behaviors

Tatzel proposed a theory of money worlds and wellbeing comprised of four prototypical consumer patterns based on whether consumers are high/low on materialism and simultaneously tight or loose with money. Tatzel proposes that the four prototypes (value-seekers, non-spenders, big-spenders, and experiencers) differ strikingly along many values, attitudes, and behaviors. This study uses data from 1,016 U.S. student consumers to test empirically the typology and differences. A cluster analysis confirmed that a four-cluster solution best represented the data, supporting Tatzel's model. Subsequent ANOVAs showed that two of the four groups differed predictably in the hypothesized directions. Significant differences between big-spenders and non-spenders appeared in levels of price sensitivity, status consumption, generosity, brand engagement, worry about debt, and spending. The other two groups, value-seekers and experiencers, fell between them. The findings partially confirm Tatzel's theory and suggest that “money worlds” are one way of conceptualizing consumer culture.

Cluster Analysis with General Latent Class Model

2011 ◽  
pp. 225-230
Author(s):  
Dingxi Qiu ◽  
Edward C. Malthouse
Keyword(s):  
Cluster Analysis ◽  
Statistical Models ◽  
Latent Variable ◽  
Latent Class ◽  
Latent Class Model ◽  
Teaching Style ◽  
Teaching Styles ◽  
Binary Responses ◽  
Cluster Membership ◽  
Wide Range

Cluster analysis is a set of statistical models and algorithms that attempt to find “natural groupings” of sampling units (e.g., customers, survey respondents, plant or animal species) based on measurements. The observable measurements are sometimes called manifest variables and cluster membership is called a latent variable. It is assumed that each sampling unit comes from one of K clusters or classes, but the cluster identifier cannot be observed directly and can only be inferred from the manifest variables. See Bartholomew and Knott (1999) and Everitt, Landau and Leese (2001) for a broader survey of existing methods for cluster analysis. Many applications in science, engineering, social science, and industry require grouping observations into “types.” Identifying typologies is challenging, especially when the responses (manifest variables) are categorical. The classical approach to cluster analysis on those data is to apply the latent class analysis (LCA) methodology, where the manifest variables are assumed to be independent conditional on the cluster identity. For example, Aitkin, Anderson and Hinde (1981) classified 468 teachers into clusters according to their binary responses to 38 teaching style questions. This basic assumption in classical LCA is often violated and seems to have been made out of convenience rather than it being reasonable for a wide range of situations. For example, in the teaching styles study two questions are “Do you usually allow your pupils to move around the classroom?” and “Do you usually allow your pupils to talk to one another?” These questions are mostly likely correlated even within a class.

scholarly journals 0577 Cluster Analysis for the Association Between Obstructive Sleep Apnea Phenotypes: A Population-Based Longitudinal Study

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A221-A221
Author(s):  
P F Tempaku ◽  
L O Silva ◽  
T M Guimaraes ◽  
T A Vidigal ◽  
V D’Almeida ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Latent Class ◽  
Cluster Solution ◽  
Optimal Care ◽  
Sleep Study ◽  
Disturbed Sleep ◽  
Obstructive Sleep

Abstract Introduction The identification of subgroups of obstructive sleep apnea (OSA) is critical to understand disease causality and ultimately develop optimal care strategies customized for each subgroup. In this sense, we aimed to perform a cluster analysis to identify subgroups of individuals with OSA based on clinical parameters. Furthermore, we aimed to analyze whether subgroups remain after 8 years. Methods We used data derived from the Sao Paulo Epidemiologic Sleep Study (EPISONO) cohort, which was followed over 8 years. All individuals underwent polysomnography, answered questionnaires and had their blood collected for biochemical exams. OSA was defined according to an AHI equal or greater than 15 events per hour. Cluster analysis was performed using latent class analysis (LCA). Results Of the 1,042 individuals in the EPISONO baseline cohort, 68.3% accepted to participate in the follow-up study (n=712). We were able to replicate the OSA 3-cluster solution observed in previous studies: disturbed sleep, minimally symptomatic and excessively sleepy in both baseline (35.5%, 45.4% and 19.1%, respectively) and follow-up studies (41.9%, 43.4% and 14.8%, respectively). 44.8% of the participants migrated clusters between the two evaluations and the factor associated with this was a greater delta-AHI (B=-0.033, df=1, p=0.003). The optimal cluster solution for our sample based on Bayesian information criterion (BIC) was 2 clusters for baseline (disturbed sleep and excessively sleepy) and 3 clusters for follow-up (disturbed sleep, minimally symptomatic and excessively sleepy). Conclusion The results found replicate and confirm previously identified clinical clusters in OSA even in a longitudinal analysis. Support This work was supported by grants from AFIP, FAPESP and CAPES.

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