The coronavirus pandemic is one of the biggest health crises of our time. In response to this global problem, various institutions around the world had soon issued evidence-based prevention guidelines. However, these guidelines, which were designed to slow the spread of COVID-19 and contribute to public well-being, are (deliberately) disregarded by some individuals. In the present study, we aimed to develop and test a multivariate model that could help us identify individual characteristics that make a person more/less likely to comply with COVID-19 prevention guidelines. A total of 525 attentive participants completed the online survey. The results of structural equation modeling (SEM) show that COVID-19 risk perception and trust in science both independently predict compliance with COVID-19 prevention guidelines, while the remaining variables in the model (political conservatism, religious orthodoxy, conspiracy ideation and intellectual curiosity) do so via the mediating role of trust in science. The described model exhibited an acceptable fit (χ²(1611) = 2485.84, p < .001, CFI = .91, RMSEA = .032, SRMR = .055). These findings thus provide empirical support for the proposed multivariate model and underline the importance of trust in science in explaining the different levels of compliance with COVID-19 prevention guidelines.

Introduction: While increasing awareness of climate change is needed to address this threat to the natural environment and humanity, it may simultaneously negatively impact mental health. Previous studies suggest that climate-specific mental health phenomena, such as climate anxiety and worry, tend to be especially pronounced in youth. To properly understand and address these issues, we need valid measures that can also be used in non-Anglophone samples. Therefore, in the present paper, we aimed to validate Slovenian versions of the Climate Anxiety Scale (CAS) and the Climate Change Worry Scale (CCWS) among Slovenian youth.Method: We conducted an online survey in which 442 young individuals (18–24 years) from Slovenia filled out the two central questionnaires and additional instruments capturing other relevant constructs (e.g., general anxiety, neuroticism, and behavioral engagement).Results: The confirmatory factor analyses results supported the hypothesized factorial structure of the CAS (two factors) and the CCWS (one factor). Both scales also demonstrated great internal reliability. Moreover, the analyses exploring both constructs’ nomological networks showed moderate positive associations with similar measures, such as anxiety and stress (convergent validity), and very weak associations with measures they should not be particularly related to, such as narcissism (discriminant validity). Lastly, we found that the CAS and, even more so, the CCWS have unique predictive value in explaining outcomes such as perceived threat, support for climate policies, and behavioral engagement (incremental validity).Discussion: Overall, Slovenian versions of the CAS and the CCWS seem to be valid, reliable, and appropriate for future studies tackling young individuals’ responses to climate change. Limitations of the study and areas for future research are discussed.

Depression is a prevalent comorbidity in patients with severe physical disorders, such as cancer, stroke, and coronary diseases. Although it can significantly impact the course of the primary disease, the signs of depression are often underestimated and overlooked. The aim of this paper was to review algorithms for the automatic, uniform, and multimodal classification of signs of depression from human conversations and to evaluate their accuracy. For the scoping review, the PRISMA guidelines for scoping reviews were followed. In the scoping review, the search yielded 1095 papers, out of which 20 papers (8.26%) included more than two modalities, and 3 of those papers provided codes. Within the scope of this review, supported vector machine (SVM), random forest (RF), and long short-term memory network (LSTM; with gated and non-gated recurrent units) models, as well as different combinations of features, were identified as the most widely researched techniques. We tested the models using the DAIC-WOZ dataset (original training dataset) and using the SymptomMedia dataset to further assess their reliability and dependency on the nature of the training datasets. The best performance was obtained by the LSTM with gated recurrent units (F1-score of 0.64 for the DAIC-WOZ dataset). However, with a drop to an F1-score of 0.56 for the SymptomMedia dataset, the method also appears to be the most data-dependent.

Background: Cancer survivors often experience disorders from the depressive spectrum that remain largely unrecognized and overlooked. Even though screening for depression is recognized as essential, several barriers prevent its successful implementation. It is possible that better screening options can be developed. New possibilities have been opening up with advances in artificial intelligence and increasing knowledge on the connection of observable cues and psychological states. Objective: The aim of this scoping meta-review was to identify observable features of depression that can be intercepted using artificial intelligence in order to provide a stepping stone toward better recognition of depression among cancer survivors. Methods: We followed a methodological framework for scoping reviews. We searched SCOPUS and Web of Science for relevant papers on the topic, and data were extracted from the papers that met inclusion criteria. We used thematic analysis within 3 predefined categories of depression cues (ie, language, speech, and facial expression cues) to analyze the papers. Results: The search yielded 1023 papers, of which 9 met the inclusion criteria. Analysis of their findings resulted in several well-supported cues of depression in language, speech, and facial expression domains, which provides a comprehensive list of observable features that are potentially suited to be intercepted by artificial intelligence for early detection of depression. Conclusions: This review provides a synthesis of behavioral features of depression while translating this knowledge into the context of artificial intelligence–supported screening for depression in cancer survivors.

Digital technologies can be a key component in helping older adults maintain their autonomy and quality of life in their homes and communities. The purpose of this scoping review was to examine the existing literature on the role of assistive digital technologies in promoting a higher quality of life and independent living for older adults by supporting their self-regulation in various aspects of daily living. The review was conducted and reported in accordance with PRISMA guidelines. Major electronic databases were searched to identify relevant articles published between 2012 and 2022. A total of 972 articles were identified, of which 19 articles met all inclusion criteria. Results are presented in four categories: (i) types of digital technologies, (ii) quality of life domains, (iii) quality of life benefits, and (iv) technological aspects supporting self-regulation. Our review also showed that successful adoption of assistive technologies depends on older adults’ trust in these technologies and the perceived benefits of technological support. Early involvement of older adults in the development of assistive technologies appears to play an important role in their technological self-efficacy. The limitations of the studies reviewed are discussed, and some general guidelines for future research in this area are suggested.