Restoring Connectedness in and to Nature: Three Nordic Examples of Recontextualizing Family Therapy to the Outdoors.

Mentalization-based family therapy and family rehabilitation represent a rich variety of approaches for assisting families with difficult interaction patterns. On the other hand, adventure therapy methods have been successfully used with families to offer them empowering experiences of succeeding together against difficult odds and to improve communication between family members. Further, the health promoting qualities of spending time outdoors are now well established and recognized. The Nordic approach to mentalization-based family rehabilitation combines adventure, outdoor, and systemic therapy. We provide three examples of nature-based family rehabilitation practices that are delivered as brief, multi-family psychological interventions taking place in nearby nature and aiming to support sustainable, systemic change. The current contribution is a description of clinical practice, not a systematic review or a formal evaluation. We propose that recontextualizing mentalization-based family rehabilitation to the outdoors can not only provide added health benefits, but also strengthen intra-familial attuned interaction and emotional connectedness. The outdoor adventure provides the families with embodied, multisensory experiences of verbal and, especially, non-verbal interaction that can be usefully examined through the lens of theory of mentalization. The concreteness of adventure experiences is particularly beneficial for families that have difficulties in verbal communication and/or utilizing executive functions, perhaps due to neuropsychiatric traits, intellectual disabilities, or learning difficulties. Furthermore, outdoor adventure can support the participants' connectedness to nature.

The Nexus between Study Burnout Profiles and Social Support -The Differences between Domestic (Finnish) and International Master's Degree Students.

The present study investigated the variation in higher education students' study burnout experiences and how they are related to academic success and social support needs. Similarities and differences between the international and domestic students were also explored. In this mixed-methods study, the data were collected through a self-reported questionnaire, and a total of 902 (response rate 42%) first year master's students from the fields of arts, business and technology responded. Using Latent Profile Analysis (LPA), we detected three distinct study burnout risk profiles (No exhaustion or cynicism; Exhausted; Exhausted and cynical). The following distinct forms of social support needs were found using theory-based qualitative content analysis: informational, instrumental, emotional, and co-constructional support. We found out that the students with highest risk of burnout had the lowest grade point averages (GPAs). Further investigation showed that international students pass their courses despite the experiences of study burnout, even though the GPAs might deteriorate. When the domestic students experience study burnout symptoms, they both gain fewer study credits and earn lower GPAs. Finally, a relationship between the form of support needed and the burnout profile was identified.

All Happy Emotions Are Alike but Every Unhappy Emotion Is Unhappy in Its Own Way: A Network Perspective to Academic Emotions.

Quantitative research into the nature of academic emotions has thus far been dominated by factor analyses of questionnaire data. Recently, psychometric network analysis has arisen as an alternative method of conceptualizing the composition of psychological phenomena such as emotions: while factor models view emotions as underlying causes of affects, cognitions and behavior, in network models psychological phenomena are viewed as arising from the interactions of their component parts. We argue that the network perspective is of interest to studies of academic emotions due to its compatibility with the theoretical assumptions of the control value theory of academic emotions. In this contribution we assess the structure of a Finnish questionnaire of academic emotions using both network analysis and exploratory factor analysis on cross-sectional data obtained during a single course. The global correlational structure of the network, investigated using the spinglass community detection analysis, differed from the results of the factor analysis mainly in that positive emotions were grouped in one community but loaded on different factors. Local associations between pairs of variables in the network model may arise due to different reasons, such as variable A causing variation in variable B or vice versa, or due to a latent variable affecting both. We view the relationship between feelings of self-efficacy and the other emotions as causal hypotheses, and argue that strengthening the students' self-efficacy may have a beneficial effect on the rest of the emotions they experienced on the course. Other local associations in the network model are argued to arise due to unmodeled latent variables. Future psychometric studies may benefit from combining network models and factor models in researching the structure of academic emotions.

Understanding the Multidimensional Nature of Student Engagement During the First Year of Higher Education.

In the description of the complex relationship between individual students and their education context, as well as understanding of questions related to progression, retention or dropouts in higher education, student engagement is considered the primary construct. In particular, the significance of the first year of higher education in terms of engagement is decisive. We aim at developing a multidimensional conceptualization of engagement and utilized network analysis. Data were collected as part of the annual Student Barometer survey in Finland during the 2012-2013 academic year, and we gathered a nationally representative sample (n = 2422) of first-year students in different disciplines at 13 Finnish universities. Network analysis confirmed the multidimensional process model of engagement and its six dimensions. The central dimensions of engagement are identity and sense of belonging, which develop in the interplay between individual and collective dimensions as a long-term process. Additional network analyses with covariates identified positive and negative factors that affect engagement. The study adds new perspectives to existing knowledge of engagement. It is important to understand the process-like nature of engagement and make visible factors affecting the process. Based on these findings, we provide novel practical recommendations for interventions for university students who struggle with engagement during their first year.

Network models of driver behavior.

The way people behave in traffic is not always optimal from the road safety perspective: drivers exceed speed limits, misjudge speeds or distances, tailgate other road users or fail to perceive them. Such behaviors are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation- and error-proneness. However, attributing dangerous behavior to stable properties of individuals may not be the optimal way of improving traffic safety, whereas investigating direct relationships between traffic behaviors offers a fruitful way forward. Network models of driver behavior and background factors influencing behavior were constructed using a large UK sample of novice drivers. The models show how individual violations, such as speeding, are related to and may contribute to individual errors such as tailgating and braking to avoid an accident. In addition, a network model of the background factors and driver behaviors was constructed. Finally, a model predicting crashes based on prior behavior was built and tested in separate datasets. This contribution helps to bridge a gap between experimental/theoretical studies and self-report-based studies in traffic research: the former have recognized the importance of focusing on relationships between individual driver behaviors, while network analysis offers a way to do so for self-report studies.

The impact of hypnotic suggestions on reaction times in continuous performance test in adults with ADHD and healthy controls.

Attention is one of the key factors in both hypnotic processes and patients with ADHD. In addition, the brain areas associated with hypnosis and ADHD overlap in many respects. However, the use of hypnosis in ADHD patients has still received only minor attention in research. The main purpose of the present work was to investigate whether hypnosis and hypnotic suggestions influence the performance of adult ADHD (n = 27) and control participants (n = 31) in the continuous performance test (CPT). The hypnotic susceptibility of the participants was measured by the Harvard Group Scale of Hypnotic Susceptibility (HGSHS:A) and the attentional task was a three minute long auditory version of the CPT. The CPT task was administered four times: before hypnosis (CPT1), after a hypnotic induction (CPT2), after suggestions about speed and accuracy (CPT3), and after the termination of hypnosis (CPT4). The susceptibility of the groups measured by HGSHS:A did not differ. There was a statistically significant decrease in reaction times in both ADHD and control groups between CPT2 and CPT3. The differences between CPT1 and CPT2, even though non-significant, were different in the two groups: in the ADHD group reaction times decreased whereas in the control group they increased. Both groups made very few errors in the short CPT. This study indicates that hypnotic suggestions have an effect on reaction times in the sustained attention task both in adult ADHD patients and control subjects. The theoretical and clinical implications are discussed.

Measurement invariance of the Driver Behavior Questionnaire across samples of young drivers from Finland and Ireland.

This article investigates the factor structure of the 27-item Driver Behavior Questionnaire (DBQ) in two samples of young drivers (18-25 years of age); one from Finland and the other from Ireland. We compare the two-, three-, and four-factor solutions using Confirmatory Factor Analysis (CFA) and show that the four-factor model (with the latent variables rule violations, aggressive violations, slips and lapses) fits the data from the two countries best. Next, we compare the fit of this model across samples by the means of a measurement invariance analysis in the CFA framework. The analysis shows that the four-factor model fails to fit both samples equally well. This is mainly because the socially-oriented latent variables (rule violations and aggressive violations) are different in nature in the two samples. The cognitively-oriented latent variables (slips and lapses) are, however, similar across countries and the mean values of slips can be compared using latent variable models. However, the common practice of calculating sum scores to represent the four latent DBQ variables and comparing them across subgroups of respondents is unfounded, at least when comparing young respondents from Finland and Ireland.

On testing factorial invariance: A reply to J.C.F. de Winter.

Since its publication, the Driver Behavior Questionnaire (DBQ) has been used for comparing subgroups of respondents on the constructs formed through factor analyzing the questionnaire items. However, not enough attention has been paid to ascertaining that the instrument actually measures the same constructs in the same way in all respondent groups. I recently published an article (Mattsson, 2012) that aimed to do this for the Finnish 28-item version of the DBQ using the stage-wise factorial invariance approach in the Exploratory Structural Equation Modeling (ESEM) context. de Winter (2013) commented on the publication, arguing that the results were artifacts due to measurement error that too many factors were extracted and that too strict criteria for invariance were applied. In this contribution, I reply to each criticism and suggest methodological approaches for ensuring the measurement invariance of self-report instruments such as the DBQ.

Investigating the factorial invariance of the 28-item DBQ across genders and age groups: an Exploratory Structural Equation Modeling study.

The Driver Behaviour Questionnaire (DBQ) is perhaps the most widely used questionnaire instrument in traffic psychology with 174 studies published by late 2010. The instrument was developed based on a plausible cognitive ergonomic theory (the Generic Error Modeling System, GEMS), but the factor structure obtained in the original study (Reason et al., 1990) did not mirror the theory's conceptual structure. This led to abandoning GEMS and adopting the obtained factor structure as a starting point for further DBQ research. This article argues that (1) certain choices in the original study, concerning statistical methodology and the wording of individual question items, may have contributed to the ways the obtained factor structure deviated from the underlying theory and (2) the analysis methods often used in DBQ studies, principal components (PC) analysis and maximum likelihood (ML) factor analysis, are not optimal choices for the non-normally distributed categorical data that is obtained using the instrument. This is because ML produces biased results when used with this type of data, while PC is by definition unable to uncover latent factors as it summarizes all variation in the measured variables. (3) Even though DBQ factor scores have been routinely compared in subgroups of men and women and respondents of different ages, DBQ's factorial invariance in these groups has not been rigorously tested. These concerns are addressed in this article by framing the results of certain previous DBQ studies as a structural equation model (SEM) and an Exploratory Structural Equation Model (ESEM) and testing measurement model fit in subgroups of respondents. The SEM analyses indicate that the model does not fit data from the whole sample of respondents as it stands, while the ESEM analyses show that a modification of the model does. However, the ESEM analyses indicate the DBQ measures different underlying latent variables in the different subgroups. Based on the analyses and a review of recent advances in attention and memory research, an update to the theory underlying the DBQ is suggested.

With power comes responsibility: motorcycle engine power and power-to-weight ratio in relation to accident risk.

OBJECTIVE: Current European legislation allows the EU member states to restrict the maximum power output of motorcycles to 74 kW even though evidence supporting the limit is scarce and has produced mixed results-perhaps because motorcycle performance has been measured by engine displacement, not engine power, in most of the previous studies. This study investigates the relationship of motorcycle engine power and power-to-weight ratio to risk of fatal and nonfatal crashes in Finland. METHODS: The fatality rate (number of fatal accidents/number of registered motorbikes) for riders of different ages riding bikes belonging to different power and power-to-weight ratio classes was examined using a comprehensive in-depth database. Data on nonfatal accidents were acquired from a Web questionnaire (N = 2708), which also served as a basis for estimating riders' annual mileage. Mileage data allowed the calculation of accident risk per kilometer ridden for bikes differing in power and power-to-weight ratio. RESULTS: The fatality risk per number of registered motorcycles and per kilometer ridden increases both with power and power-to-weight ratio, independently of rider's age. No relationship between performance and risk of a less severe crash was found. The pre-accident speed of the most powerful bikes was 20 km/h or more over the speed limit in a large proportion of the fatal accidents (odds ratio = 4.8 for > 75 kW motorbikes; odds ratio = 6.2 for > 0.3 kW/kg motorbikes). CONCLUSION: The risk of being involved in a fatal crash is higher among the riders of powerful motorcycles. However, it is not clear whether the results are related to the riding habits of the riders that choose the most powerful bikes available or whether the high risk is due to the properties of the bikes themselves. Therefore, further research is needed before considering legal limits on motorcycle performance.