Janine Bacher, Christina Stadler, Eva Unternaehrer, Donja Brunner

Department of Child and Adolescent Psychiatry, University of Basel

Background: Adolescent refugees are particularly vulnerable to mental health problems, as they experience many risk factors at crucial stages of their physical and emotional development. Despite having a greater healthcare requirement than others, they face significant barriers to accessing healthcare services. Therefore, this study aims to test the effectiveness of a low-threshold, culturally adapted version of the skills training START NOW – START NOW Adapted - in reducing mental health problems among adolescent refugees.
Methods: We will recruit 80 adolescent refugees (15 to 18years) with symptoms of anxiety and depression or high perceived stress in Northwestern Switzerland. They will be randomly assigned to one of two study groups: an intervention group, receiving START NOW Adapted, and a control group, receiving treatment as usual. The intervention will last 10 weeks and will consist of one-hour sessions per week provided by a trained facilitator with the same cultural background in the respective language. Assessments to collect depressive and anxious symptoms, perceived stress, social-ecological resilience, and emotion recognition abilities will be conducted pre-intervention, post-intervention (11 weeks later) and at the 3-month follow-up. Multilevel models will be computed with primary and secondary outcome measures as dependent variables.
Discussion: This randomized controlled trial is the first to date to investigate the effectiveness of the culturally adapted skills training START NOW, providing valuable insights to improve current health promotion of adolescent migrants in Switzerland. Ultimately, effects of START NOW Adapted may facilitate integration and promote healthy development while decreasing costs associated with treating migration- or conflict-related trauma.

Lukas Engel, Ola Elassal, Mario Herberz, Ulf Hahnel          

Psychology of Sustainability and Behavior Change, University of Basel

The widespread adoption of battery electric vehicles (BEV) is a crucial step towards decarbonizing the transportation sector. Yet and despite purchase price decreases, technological advancements, and loading infrastructure upgrades, the current BEV diffusion is insufficient to meet emission mitigation targets and shows high spatial variance across and within countries. Therefore, psychological barriers seem to play an increasingly important role in hindering BEV uptake. In a US sample, we find evidence for significant underestimations of the battery range compatibility with individuals’ own mobility needs and the carbon emission benefits compared to conventional vehicles, as well as a significant overestimation of the yearly operating costs of BEVs compared to conventional vehicles. To counteract these misperceptions, we tested four tailored interventions which presented either one correcting information (i.e., actual range compatibility, actual difference in life cycle carbon emissions, and actual difference in yearly operating costs) or all three information combined. Their effectiveness was assessed based on seven willingness to pay measurements corresponding to seven battery ranges between 60 and 240 miles, while the reference point for the evaluation was a conventional vehicle. Across multiple analyses, we found robust positive effects of the intervention that combined all information and the range compatibility intervention, while findings for the operating costs intervention were mixed, and for the environmental benefits indicative of ineffectiveness. Based on these findings, adjustments to the interventions will be made to eventually investigate the spatial heterogeneity of BEV adoption within the US in future studies. This is supposed to inform effective policy-making.

Hanzhang Xie, Karin Hediger    

Clinical Psychology and Psychotherapy, University of Basel; Faculty of Behavioural Sciences and Psychology, University of Lucerne           

Individuals face an increasingly large number of social and technological risks. How these risks are perceived is of critical interest for researchers and policymakers alike. In recent years, researchers have demonstrated that high-dimensional word embeddings derived from vast online text corpora can improve our understanding of, and ability to predict risk perception. Most notably, Bhatia (2019) has recently found machine learning models such as Word2Vec to be on par with more traditional, psychometric approaches to risk perception. However, text may not present the best available source of information to capture people's semantic representations. Research has found representations derived from free associations to be a promising alternative when predicting human judgments and behaviour than those derived from text. We begin by presenting a reanalysis of Bhatia (2019) but with two main differences. Firstly, to ensure a representative, up-to-date evaluation of text-based accounts, we employ more recent text-based models, which benefit from architectural improvements and a larger training set. Secondly, we add our own free association-based representations to the model comparison, trained on data from the Small World of Words study in English dataset. To train this model, we apply singular value decomposition to the positive pointwise mutual information-transformed cue-associate matrix. We then use cross-validation to evaluate the different representations’ ability to predict risk perception, and find that the free association-based representations are on par with the best performing text-based models, despite being trained on orders of magnitudes less data. We also find that an ensemble of the psychometric and semantic models can explain over 85% of the risk perception variance. Furthermore, analyses of how the semantic models’ predictive accuracy scale with the amount of training data indicates that higher performance can be achieved with participant ratings for a larger training set of risk sources than the 306 sources available in Bhatia’s data.  As such, in our main study, we collect risk judgements for a set of 1006 risk sources, and evaluate model performance on this larger set of sources. Finally, we demonstrate the applicability of these newly fitted representations by employing them to analyse the riskiness of the language used in online articles – namely, UK parliament speeches – identifying patterns of changing risk perception over time that correspond to real-world events.

Keywords: risk perception, free association, semantic representation