Search

The aim of the present study was to identify profiles of elite athlete mental well- and ill-being and study how the profiles (i.e., subgroups of athletes) differed in sport-related demands and resources. A total of 259 Finnish elite athletes (n = 170 active and n = 89 retired) completed quantitative self-report inventories. Through cluster analysis, four profiles of mental well- and ill-being were identified. Profile 1 was overrepresented by retired, older, and male athletes, and characterized by good mental well-being. Profile 2 consisted mainly of active athletes who reported mild risk for alcohol abuse. Profile 3 consisted mainly of women who displayed possible presence of an eating disorder. Profile 4 was typical of young athletes with mental ill-being. The balance between sport-related demands and resources appeared to be the healthiest in Profile 1 and worst in Profile 4. The present findings are beneficial for those who work with and/or provide psychological support to athletes.

We pursue Overwatch player profiles via a statistical cluster analysis of survey data from the UK (N = 1089) and the USA (N = 417). The profiles are based on the players’ activity, challenge, and experiential preferences as well as motivations. Our analytical process produces six esports player clusters, two of which with Overwatch. The first (OW1) plays mainly Overwatch and Fortnite on a console, and they enjoy diverse types of non-competitive play elements more than other esports players. The second cluster (OW2) plays mainly Overwatch and League of Legends on a PC, and despite appearing more “competitive”, they did not report more competitive preferences. We suggest that the alleged “casual” and “competitive” types might not be the best way to identify esports and Overwatch players.

The steroidal module of the Athlete Biological Passport (ABP) aims to detect doping with endogenous steroids, e.g. testosterone (T), by longitudinally monitoring several biomarkers. These biomarkers are ratios combined of urinary concentrations of testosterone and metabolically related steroids. However, it is evident after five years of monitoring steroid passports, that there are large variations in the steroid ratios complicating its interpretation. In this study, we used over 11 000 urinary steroid profiles from Swedish and Norwegian athletes to determine both the inter‐ and intra‐individual variations of all steroids and ratios in the steroidal passport. Furthermore, we investigated if the inter‐individual variations could be associated with factors such as gender, type of sport, age, time of day, time of year and if the urine was collected in or out of competition. We show that there are factors reported in today's doping tests that significantly affect the steroid profiles. The factors with the largest influence on the steroid profile was what type of sport classification the athlete belonged to as well as if the urine was collected in or out of competition. There were also significant differences based on what time of day and time of year the urine sample was collected. If these significant changes are relevant when longitudinally monitoring athletes in the steroidal module of the ABP, has to be further evaluated. peerReviewed

Research into differentiation and profiling of knowledge producing institutions through the lenses of institutional logics and field embeddedness have proliferated in recent years. By discussing this process in the context of research groups, as those basic units in which knowledge production epistemically and practically takes place, this article offers a contribution to the theoretical discussion on organisational differentiation. Based on a small-N comparative case study of research groups operating in different national and organisational contexts on a single, highly competitive and interdisciplinary applied sciences field, nanosciences, the article proposes research group profiles as heuristic devices to explore both the embeddedness and strategic agency of research groups. peerReviewed

Latent profile analysis (LPA) is a person-centered method commonly used in organizational research to identify homogeneous subpopulations of employees within a heterogeneous population. However, in the case of nested data structures, such as employees nested in work departments, multilevel techniques are needed. Multilevel LPA (MLPA) enables adequate modeling of subpopulations in hierarchical data sets. MLPA enables investigation of variability in the proportions of Level 1 profiles across Level 2 units, and of Level 2 latent classes based on the proportions of Level 1 latent profiles and Level 1 ratings, and the extent to which covariates drawn from the different hierarchical levels of the data affect the probability of a membership of a particular profile. We demonstrate the use of MLPA by investigating job characteristics profiles based on the job-demand-control-support (JDCS) model using data from 1,958 university employees clustered in 78 work departments. The implications of the results for organizational research are discussed, together with several issues related to the potential of MLPA for wider application. peerReviewed