There is growing evidence that climate anxiety is associated with significant effects on the mental health and wellbeing of young people. However, the relative importance of climate anxiety for young people's mental health has hitherto been unclear, as climate anxiety has largely been studied in isolation from other common stressors. This study sought to contextualize the significance of climate anxiety for the mental health of UK young adults relative to other concurrent psychological stressors. We surveyed university students (N = 461) and a general population sample aged 18-25 (N = 400). The results showed that while climate anxiety was significantly associated with poorer mental health and worse insomnia when examined alone, this association became nonsignificant or greatly diminished when other stressors were considered. Loneliness was found to be the most important predictor of mental health, and financial anxiety the most important predictor of insomnia severity. The findings suggest that climate anxiety, while concerning, may not be an especially dominant factor in young people's mental health. Our research highlights the need to consider the broader context of young people's lives, and the complex interplay of various psychological stressors, in efforts to map pathways between climate change and mental health.

Machine-assisted approaches for free-text analysis are rising in popularity, owing to a growing need to rapidly analyze large volumes of qualitative data. In both research and policy settings, these approaches have promise in providing timely insights into public perceptions and enabling policymakers to understand their community's needs. However, current approaches still require expert human interpretation-posing a financial and practical barrier for those outside of academia. For the first time, we propose and validate the Deep Computational Text Analyser (DECOTA)-a novel machine learning methodology that automatically analyzes large free-text data sets and outputs concise themes. Building on structural topic modeling approaches, we used two fine-tuned large language models and sentence transformers to automatically derive "codes" and their corresponding "themes", as in inductive thematic analysis. To fully automate the process, we designed and validated a novel algorithm to choose the optimal number of "topics" for the structural topic modeling. DECOTA outputs key codes and themes, their prevalence, and how prevalence varies across covariates such as age and gender. Each code is accompanied by three representative quotes. Four data sets previously analyzed using thematic analysis were triangulated with DECOTA's codes and themes. We found that DECOTA is approximately 378 times faster and 1,920 times cheaper than human coding and consistently yields codes in agreement with or complementary to human coding (averaging 91.6% for codes and 90% for themes). The implications for evidence-based policy development, public engagement with policymaking, and psychometric measure development are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Environmental knowledge is considered an important pre-cursor to pro-environmental behaviour. Though several tools have been designed to measure environmental knowledge, there remains no concise, psychometrically grounded measure. We validated an existing measure in a British sample, confirming that it had good one- and three-factor structures in line with previous literature. For the first time in this field, we built upon previous Classical Test Theory approaches and used discrimination values derived from Item Response Theory to select the best items, resulting in the 19-Item Environmental Knowledge Test (EKT-19). This measure retained a clear factor structure and had moderate-to-good internal reliability, indicating that it is a parsimonious and psychometrically robust measure for the assessment of overall and specific types of environmental knowledge. The theoretical implications and real-world applications of this measure are discussed.

As ambient air pollution increases, governments are imposing traffic management strategies to improve air quality. A common strategy is the implementation of Low Emission Zones (LEZs), which have generated considerable public debate. Nonetheless, little research has explored which factors determine their public acceptability. Previous empirical studies have also typically lacked power for regression analyses and have not determined the relative importance of different predictors. After conducting a large online survey in a UK city, well-powered multiple regression and dominance analyses demonstrated that psychological factors, such as environmental moral obligation, were the most important predictors of LEZ acceptability. However, travel-related and socio-demographic factors, such as distance lived from the LEZ and having dependent children, were also unique and important predictors. Overall, we argue that, whilst psychological factors are important, travel-related and socio-demographic barriers must not be overlooked during LEZ implementation.

As scientific evidence of the severity of climate change increases, there are indications that this represents a significant psychological burden in the form of climate anxiety on the public. So far very little research has explored the prevalence, predictors, or effects of climate anxiety amongst the public. This study aims to address this gap by exploring climate anxiety in the UK. It addresses the following questions: (a) How prevalent is climate anxiety amongst adults in the UK? (b) What are the predictors of climate anxiety? and (c) Does climate anxiety predict climate action? We report on findings from an online survey of the UK public (N = 1338) undertaken in late 2020 (partially replicated in May 2022 with a sub-sample of 891 respondents) which found that while there are high levels of concern about climate change, there are low levels of climate anxiety (measured using the Climate Change Anxiety Scale). Climate anxiety was higher amongst younger age groups, those with higher climate concern, higher generalised anxiety, lower mindfulness, higher nature relatedness, and more climate change information seeking behaviour. In addition, climate anxiety predicted some (but not all) types of pro -environmental action. Consistent with other recent research, these findings indicate that climate anxiety may not necessarily be a negative impact of, or maladaptive response to, climate change; but rather, at least to some degree, be a motivating force for effective action.

Karmic belief-the expectation that actions bring about morally congruent outcomes within and across lifetimes-is central to many, particularly Eastern, religions. This research (N = 704) examined whether karmic beliefs and cultural context (predominantly Christian Americans and Hindu Indians) moderated the indirect effect of learning about others' morally congruent or incongruent negative outcomes on immanent justice reasoning (causally attributing misfortune to someone's prior misdeeds) through perceived deservingness. Results revealed that the indirect effect of congruency on immanent justice attributions via deservingness was stronger for people higher in karmic beliefs, because they strengthened the effect of congruency on immanent justice attributions and the relationship between deservingness and immanent justice attributions. The indirect effect of congruency on immanent justice attributions through deservingness was also stronger in the United States. These results highlight the role that karmic beliefs play in how people reason about the causes of others' fortunes and misfortunes.

It has been proposed that atypical empathy in autism spectrum disorder (ASD) is due to co-occurring alexithymia. However, difficulties measuring empathy and statistical issues in previous research raise questions about the role of alexithymia in empathic processing in ASD. Addressing these issues, we compared the associations of trait alexithymia and autism with empathy in large samples from the general population. Multiple regression analyses showed that both trait autism and alexithymia were uniquely associated with atypical empathy, but dominance analysis found that trait autism, compared to alexithymia, was a more important predictor of atypical cognitive, affective, and overall empathy. Together, these findings indicate that atypical empathy in ASD is not simply due to co-occurring alexithymia.