Departament de Psicologia [Ressenya del llibre Adolescents and audio-visual media in five countries, de Ferran Casas, Irene Rizzini, Rose September, Per Egil Mjaavatn i Usha Nayar (coord.)]

Aquest llibre és el producte d'anys de cooperació entre equips de recerca de cinc països diferents, tot ells Key Institutions de la xarxa Childwatch International, en el marc d'un projecte plurinacional sobre adolescents i mitjans

L'Exercici de la docència de la psicologia i el Burnout

En l’actualitat el burnout o síndrome de cremar-se per la feina és considerat un risc emergent als països occidentals essent el col·lectiu docent un dels més afectats. L’espai europeu d’educació superior proposa un nou rol pel professor que haurà de ser compaginat amb la gestió, la recerca i la transferència de coneixement. En la present recerca s’analitza el nivell de burnout en una mostra formada per 42 docents (mitjana d’edat: 37,21 anys; D.T.: 8,98; 70,8% dones) del departament de Psicologia de la Universitat de Girona. El burnout s’ha avaluat amb el Maslach Burnout Inventory (MBI) així com amb un qüestionari d’elaboració pròpia que recull variables sociodemogràfiques, característiques del tipus de treball i de l’ús del temps lliure, el locus de control i el burnout percebut. Els resultats obtinguts indiquen valors mitjans en les puntuacions de les dimensions Cansament Emocional i Despersonalització, i valors elevats en Realització Personal. El 20,5% dels docents es percep cremat per la feina

Networks of PhD Students and Academic Performance: A Comparison across Countries

In this article we compare regression models obtained to predict PhD students’ academic performance in the universities of Girona (Spain) and Slovenia. Explanatory variables are characteristics of PhD student’s research group understood as an egocentered social network, background and attitudinal characteristics of the PhD students and some characteristics of the supervisors. Academic performance was measured by the weighted number of publications. Two web questionnaires were designed, one for PhD students and one for their supervisors and other research group members. Most of the variables were easily comparable across universities due to the careful translation procedure and pre-tests. When direct comparison was not possible we created comparable indicators. We used a regression model in which the country was introduced as a dummy coded variable including all possible interaction effects. The optimal transformations of the main and interaction variables are discussed. Some differences between Slovenian and Girona universities emerge. Some variables like supervisor’s performance and motivation for autonomy prior to starting the PhD have the same positive effect on the PhD student’s performance in both countries. On the other hand, variables like too close supervision by the supervisor and having children have a negative influence in both countries. However, we find differences between countries when we observe the motivation for research prior to starting the PhD which increases performance in Slovenia but not in Girona. As regards network variables, frequency of supervisor advice increases performance in Slovenia and decreases it in Girona. The negative effect in Girona could be explained by the fact that additional contacts of the PhD student with his/her supervisor might indicate a higher workload in addition to or instead of a better advice about the dissertation. The number of external student’s advice relationships and social support mean contact intensity are not significant in Girona, but they have a negative effect in Slovenia. We might explain the negative effect of external advice relationships in Slovenia by saying that a lot of external advice may actually result from a lack of the more relevant internal advice

PhD Students’ Research Group Social Capital in Two Countries: A Clustering Approach with Duocentred Network Measures

The article examines the structure of the collaboration networks of research groups where Slovenian and Spanish PhD students are pursuing their doctorate. The units of analysis are student-supervisor dyads. We use duocentred networks, a novel network structure appropriate for networks which are centred around a dyad. A cluster analysis reveals three typical clusters of research groups. Those which are large and belong to several institutions are labelled under a bridging social capital label. Those which are small, centred in a single institution but have high cohesion are labelled as bonding social capital. Those which are small and with low cohesion are called weak social capital groups. Academic performance of both PhD students and supervisors are highest in bridging groups and lowest in weak groups. Other variables are also found to differ according to the type of research group. At the end, some recommendations regarding academic and research policy are drawn