Implementation and Preliminary Outcomes of an Exposure-Based Summer Camp for Pediatric OCD and Anxiety.

Despite the high prevalence of anxiety disorders in children and adolescents and the existence of effective evidence-based treatments for them, access to psychological care remains a major public health concern. Summer camps may provide an effective treatment avenue for youth who might not otherwise have access to care. This study describes the design and implementation of Fear Facers, a semistructured, 5-day, daytime exposure-therapy-based summer camp designed for youth with a primary diagnosis of obsessive-compulsive disorder (OCD), social anxiety, separation anxiety, or a specific phobia. Preliminary data regarding feasibility and patient outcomes is also reported. Among 52 children and adolescents aged 7 to 16 who attended one of six camp sessions between 2018 and 2021, significant reductions in anxiety (d = 0.54) and OCD symptoms (d = 0.57) were observed from pre-camp to immediately post-camp. A subset of campers who were followed for an additional 3 months post-camp (n = 22) showed maintenance of treatment gains. Retention rates for the intervention were high. Our investigation provides further support for the use of a camp-based design for cognitive-behavioral approaches, and may provide a unique setting to maximize elements of inhibitory learning in exposures. We also discuss a number of elements regarding feasibility that need consideration for those hoping to develop similar interventions.

Expectations and perspectives of cognitive behavioural therapy for childhood anxiety and related disorders.

BACKGROUND: Anxiety disorders are the most frequently diagnosed psychiatric conditions in children and adolescents. Cognitive behavioural therapy (CBT) is a well-established and effective treatment for anxiety and related disorders across the lifespan. Expectations of psychotherapy have been demonstrated to affect outcomes, yet there is sparse existing literature on adolescent patient and parent perspectives of CBT prior to engagement with treatment. AIMS: This study aimed to qualitatively explore the expectations and perceptions of CBT for anxiety and related disorders among adolescent patients and parents. METHOD: Fourteen adolescent patients and 16 parents participated in semi-structured individual interviews or focus groups consisting of 2-3 participants. Interview transcripts were analysed using inductive analysis. RESULTS: Three themes were identified: worries about CBT, expectations and knowledge of the CBT process, and the role of parents and families. Overall, we found that adolescents and parents had generally positive views of CBT. The outset of CBT saw adolescents and parents express concern about stigma as well as the ambiguity of CBT. Parents continued to express a lack of understanding of what CBT entailed during their child's treatment course. CONCLUSION: These results suggest that both adolescents and parents would benefit from early discussion and reinforcement of expectations for CBT treatment. Further research efforts are warranted and should be directed towards determining appropriate expectations for parental involvement in a child's CBT course and effective communication of treatment expectations to both adolescents and parents.

Relationships between stress appraisals and intolerance of uncertainty with psychological health during early COVID-19 in the USA.

BACKGROUND AND OBJECTIVES: The COVID-19 pandemic has caused increased psychological distress and decreased quality of life (QoL), but limited research has evaluated cognitive appraisals of COVID-19. This study aimed to examine the role of three stress appraisals of the COVID-19 pandemic - threat appraisal, harm/loss appraisal, and challenge appraisal - and the interaction between these appraisals and intolerance of uncertainty (IU) on psychological distress and QoL. DESIGN: This was a cross-sectional survey study using Amazon's Mechanical Turk. METHODS: A sample of 327 adults living in the U.S. completed a series of questionnaires investigating IU, appraisal of the pandemic, psychological distress, and QoL in May 2020 during the first wave of the COVID-19 pandemic. RESULTS: Controlling for demographic variables, IU and harm/loss appraisal positively related to psychological distress and negatively related to QoL. Challenge appraisals negatively related to distress and positively related to QoL. Moderation analyses revealed that higher levels of IU led to stronger positive relationships between threat appraisal and psychological distress. CONCLUSIONS: Cognitive appraisals of COVID-19 are associated with psychological health, though the relationship between threat appraisal and psychological distress are strongest for those with difficulty tolerating uncertainty. Theoretical and practical implications are discussed.

Assessing the predictors of adaptive and maladaptive Covid-19 preventive behaviours: an application of protection motivation theory.

Despite the high death toll of the COVID-19 pandemic, reported rates of adherence to adaptive preventive health behaviours during the early wave of the pandemic were suboptimal for reducing the risk of disease spread. Additionally, some have adopted practices with the intention of preventing infection that have harmful consequences. Protection Motivation Theory (PMT), consisting of perceived vulnerability, severity, response efficacy, and self-efficacy, has been used to predict intentions to engage in behaviours in past pandemics, and can be extended to the COVID-19 outbreak. Three hundred and thirty-three American adults completed a survey in May 2020 through Amazon's Mechanical Turk. Ten behaviours recommended by the CDC and WHO and two 'maladaptive' behaviours presented in the media were selected for investigation. Binary logistic regressions were conducted to assess the impacts of demographic variables and PMT constructs on behaviour frequency. Perceived severity and vulnerability were not significant predictors of behaviour frequency. Behaviour specific response efficacy and self-efficacy were significant predictors of 11/12 (odds ratios: 2.70-6.22) and 10/12 (odds ratios: 2.59-4.64) behaviours, respectively. Age, gender, education, political ideology, perceived severity, and perceived vulnerability were generally unimportant predictors. Beliefs about the effectiveness of the behaviour and one's ability to carry out that behaviour consistently seem to be more important in predicting how often someone engages in that behaviour than the perceived dangerousness of COVID-19 and one's believed susceptibility to infection. These results suggest that interventions trying to modulate the likelihood of engaging in preventive behaviours should focus on the effectiveness of these behaviours in reducing risk of spread and the individual's ability to engage in these behaviours frequently rather than the dangerousness of the COVID-19 pandemic and the individual's risk of becoming infected.

Approach and Avoidance Coping Dynamics during the COVID-19 Pandemic.

Approach-avoidance conflicts were one of the earliest applications of catastrophe theory. Empirical studies evaluating the cusp catastrophe model for approach-avoidance dynamics have only started to appear recently, however. The present study reviews the extant research and expands the concept to approach and avoidance coping styles. Research participants were 333 adults from the general population recruited through Amazon Mechanical Turk. They completed measures of psychological symptoms, quality of life (QOL), approach and avoidance coping styles, and COVID-related stress. Cusp models for symptoms (R2 = .84) and QOL (R2 = .89) illustrated approach and avoidance functioning as bifurcation gradients for both psychological symptoms and QOL. Both models provided more accurate representations of the data than the linear alternatives (R2 = .54 and .24 respectively), thus providing further support for the cusp dynamics. The cusp catastrophe model has extensive applicability to approach-avoidance behaviors. There was greater variability (hysteresis) in outcomes for people who used fewer coping strategies of either the approach or avoidance types.

An examination of obsessive-compulsive symptom domains, depression, and quality of life within an online survey sample.

Obsessive-compulsive disorder (OCD) is a heterogeneous and highly impairing disorder that is frequently comorbid with other conditions. Participants in this study were 212 individuals recruited through Mechanical Turk who filled out validated measures of obsessive-compulsive symptoms, quality of life (QoL), generalized anxiety, and depressive symptoms. Analyses examined the influences of each symptom variable on QoL and the mediating role of depression as an indirect link between unacceptable thoughts (UT) and QoL. Depressive symptoms had a significant negative relationship with multiple domains of functioning. Generalized anxiety was not significant. Higher endorsement of UT symptoms was related to lower physical, emotional, and social QoL. Depression partially mediated the relationship between UT symptoms and physical, emotional, and social health. Depressive symptoms are important to consider in clinical work surrounding OCD. The significant associations between UT symptoms and QoL in a nonclinical population illustrate a relevant area for future intervention, public awareness, and education.

KS-pies: Kohn-Sham inversion toolkit.

A Kohn-Sham (KS) inversion determines a KS potential and orbitals corresponding to a given electron density, a procedure that has applications in developing and evaluating functionals used in density functional theory. Despite the utility of KS inversions, application of these methods among the research community is disproportionately small. We implement the KS inversion methods of Zhao-Morrison-Parr and Wu-Yang in a framework that simplifies analysis and conversion of the resulting potential in real-space. Fully documented Python scripts integrate with PySCF, a popular electronic structure prediction software, and Fortran alternatives are provided for computational hot spots.

Learning to Approximate Density Functionals.

Density functional theory (DFT) calculations are used in over 40,000 scientific papers each year, in chemistry, materials science, and far beyond. DFT is extremely useful because it is computationally much less expensive than ab initio electronic structure methods and allows systems of considerably larger size to be treated. However, the accuracy of any Kohn-Sham DFT calculation is limited by the approximation chosen for the exchange-correlation (XC) energy. For more than half a century, humans have developed the art of such approximations, using general principles, empirical data, or a combination of both, typically yielding useful results, but with errors well above the chemical accuracy limit (1 kcal/mol). Over the last 15 years, machine learning (ML) has made major breakthroughs in many applications and is now being applied to electronic structure calculations. This recent rise of ML begs the question: Can ML propose or improve density functional approximations? Success could greatly enhance the accuracy and usefulness of DFT calculations without increasing the cost.In this work, we detail efforts in this direction, beginning with an elementary proof of principle from 2012, namely, finding the kinetic energy of several Fermions in a box using kernel ridge regression. This is an example of orbital-free DFT, for which a successful general-purpose scheme could make even DFT calculations run much faster. We trace the development of that work to state-of-the-art molecular dynamics simulations of resorcinol with chemical accuracy. By training on ab initio examples, one bypasses the need to find the XC functional explicitly. We also discuss how the exchange-correlation energy itself can be modeled with such methods, especially for strongly correlated materials. Finally, we show how deep neural networks with differentiable programming can be used to construct accurate density functionals from very few data points by using the Kohn-Sham equations themselves as a regularizer. All these cases show that ML can create approximations of greater accuracy than humans, and is capable of finding approximations that can deal with difficult cases such as strong correlation. However, such ML-designed functionals have not been implemented in standard codes because of one last great challenge: generalization. We discuss how effortlessly human-designed functionals can be applied to a wide range of situations, and how difficult that is for ML.

Bypassing the Energy Functional in Density Functional Theory: Direct Calculation of Electronic Energies from Conditional Probability Densities.

Density functional calculations can fail for want of an accurate exchange-correlation approximation. The energy can instead be extracted from a sequence of density functional calculations of conditional probabilities (CP DFT). Simple CP approximations yield usefully accurate results for two-electron ions, the hydrogen dimer, and the uniform gas at all temperatures. CP DFT has no self-interaction error for one electron, and correctly dissociates H_{2}, both major challenges. For warm dense matter, classical CP DFT calculations can overcome the convergence problems of Kohn-Sham DFT.

Cleanup and Complexity: Nuclear and Industrial Contamination at The Santa Susana Field Laboratory, California.

Environmental contamination, a legacy of industrial activity borne by numerous sites around the world, poses health risks for surrounding communities and presents serious cleanup challenges. One such site, the Santa Susana Field Laboratory (SSFL), served as an aerospace and nuclear energy research facility for over 50 years, during which time radioactive and other hazardous materials were unintentionally and intentionally released into the surrounding environment. These releases, including the partial meltdown of a sodium reactor, were hidden from the public for three decades. The site is now located in suburban Los Angeles, with 730,000 people living within a 10-mile radius. This paper evaluates the technical and social challenges underlying site cleanup at SSFL, including a complex geological setting, uncertain contaminant information, and a convoluted, evolving regulatory framework. These challenges, paired with historical secrecy on the part of responsible organizations and unclear layers of responsibility, have led to uncertainty and distrust within the surrounding community. Lessons learned from other remediated sites are assessed and recommendations for the SSFL cleanup are provided.

Solid-state NMR and short-range order in crystalline oxides and silicates: a new tool in paramagnetic resonances.

Most applications of high-resolution NMR to questions of short-range order/disorder in inorganic materials have been made in systems where ions with unpaired electron spins are of negligible concentration, with structural information extracted primarily from chemical shifts, quadrupolar coupling parameters, and nuclear dipolar couplings. In some cases, however, the often-large additional resonance shifts caused by interactions between unpaired electron and nuclear spins can provide unique new structural information in materials with contents of paramagnetic cations ranging from hundreds of ppm to several per cent and even higher. In this brief review we focus on recent work on silicate, phosphate, and oxide materials with relatively low concentrations of paramagnetic ions, where spectral resolution can remain high enough to distinguish interactions between NMR-observed nuclides and one or more magnetic neighbors in different bonding configurations in the first, second, and even farther cation shells. We illustrate the types of information available, some of the limitations of this approach, and the great prospects for future experimental and theoretical work in this field. We give examples for the effects of paramagnetic transition metal, lanthanide, and actinide cation substitutions in simple oxides, pyrochlore, zircon, monazite, olivine, garnet, pyrochlores, and olivine structures.

Investigating lanthanide dopant distributions in Yttrium Aluminum Garnet (YAG) using solid state paramagnetic NMR.

This paper demonstrates the approach of using paramagnetic effects observed in NMR spectra to investigate the distribution of lanthanide dopant cations in YAG (yttrium aluminum garnet, Y3Al5O12) optical materials, as a complimentary technique to optical spectroscopy and other standard methods of characterization. We investigate the effects of Ce3+, Nd3+, Yb3+, Tm3+, and Tm3+-Cr3+ on 27Al and 89Y NMR spectra. We note shifted resonances for both AlO4 and AlO6 sites. In some cases, multiple shifted peaks are observable, and some of these can be empirically assigned to dopant cations in known configurations to the observed nuclides. In many cases, AlO6 peaks shifted by more than one magnetic neighbor can be detected. In general, we observe that the measured intensities of shifted resonances, when spinning sidebands are included, are consistent with predictions from models with dopant cations that are randomly distributed throughout the lattice. In at least one set of 27Al spectra, we identify two sub-peaks possibly resulting from two paramagnetic cations with magnetically coupled spin states neighboring the observed nucleus. We identify systematic changes in the spectra related to known parameters describing the magnetic effects of lanthanide cations, such as larger shift distances when the expectation value of electron spins is greater. We lastly comment on the promise of this technique in future analyses of laser and other crystalline oxide materials.