Psychological symptoms tend to change over time, even in the absence of clinical intervention. For example, self-ratings are often higher at screening compared to start of treatment. A plausible hypothesis is that this is due to patients' self-referring when their gradually fluctuating symptoms are worse than usual. That hypothesis predicts that patients that wait longer will have had longer time to return to their average symptom level. On the other hand, other processes related to measurement reactivity, contact with a clinician, or regression towards the mean, do not predict a time-dependent relationship. Our aim was to estimate the extent of this hypothesized symptom reduction in depression, social anxiety disorder, panic disorder, health anxiety and insomnia (both total reduction and the relationship with time). The sample included adults (N = 8744) from an outpatient psychiatric clinic providing ICBT in Swedish routine care. Time-dependent effects were estimated with linear regression for both primary symptoms and secondary depressive symptoms. A simulation of symptom fluctuations was built to estimate power and further contextualize the effects. Patients improved on average from screening to the start of the intervention, but this varied substantially depending on diagnosis and questionnaire used. The waiting time weakly predicted the degree of improvement both for primary depressive symptoms and comorbid depressive symptoms. The estimate for primary depressive symptoms was sensitive to modeling choices, shrinking towards zero when modeled with fat-tailed residuals. The preponderance of "immediate" reductions in symptoms have implications for reporting standards of pretreatment-measurements, especially in single-group intervention studies.

As climate change raises temperatures, projecting future building energy demand is crucial for adaptation, mitigation, and resilient infrastructure and energy systems. This is intensified by Europe's ageing population, including Norway, where more elderly residents will increase demand for elderly housing and vulnerability to thermal stress. This study examines how climate change affects the energy demand while ensuring thermal comfort of the elderly in newly built elderly housing in Baerum Municipality, Norway. Urban Building Energy Modelling was used to simulate heating and cooling energy demand in two elderly housing complexes under Intergovernmental Panel on Climate Change Assessment Report 5 climate scenarios, Representative Concentration Pathways (RCP 2.6 and RCP 8.5) for the 2030s-2060s. Custom weather datasets included a 2023 Actual Meteorological Year (AMY) for calibration, a Typical Meteorological Year (TMY) baseline, and future weather files for different greenhouse gas trajectories. Contemporary and projected extreme weather data were generated to assess building energy performance under current and severe climate conditions. The RCP 8.5 scenario is projected to reduce heating demand by up to 19% by the 2060s, while the RCP 2.6 scenario reduces it by about 9% relative to historical data from 2000 to 2020. Peak cooling demand is forecasted to increase by 34% by the 2060s with more frequent and longer cooling events. This indicates a growing need for infrastructure improvements to meet future energy demands. Understanding these shifts in energy demand is crucial for preparedness to ensure the well-being and thermal comfort of elderly populations.

Telepsychiatry is increasingly used for mental health triage, but real-world implementation remains underreported, and barriers include technical reliability, patient trust, privacy and coordination across levels of care. Objective To describe and evaluate an app- and video-based triage service, Immediate Psychiatry (IP), implemented in Region Stockholm, Sweden, focusing on service use, patient characteristics, acceptability, feasibility and team experiences. Methods Adults (>= 18) registered with participating primary care centres accessed IP via the regional health app, completed a digital brief screening questionnaire, and had a 45-min video consultation with a mental health professional. A mixed-methods design was employed combining medical-record abstractions, a patient-satisfaction survey, interviews with the IP team and a clinician acceptability questionnaire administered to primary care clinicians. Quantitative data were analysed descriptively; qualitative data underwent content analysis. Results A total of 172 participants were included, predominantly presenting with stress-related, depressive and anxiety disorders. High patient satisfaction was reported for IP, particularly in terms of ease of contact, patient involvement and respectful treatment. The IP team described that the triage process was challenging yet meaningful, relying heavily on an interprofessional team with strong clinical expertise and a person-centred approach. Improved integration with regular care could further enhance the service. The response rate among primary care clinicians was low, with responses indicating moderate acceptability. Conclusions Telepsychiatry for triage delivered through a regional app was feasible, acceptable and may improve access to timely assessments. The findings also highlight important organizational and implementation challenges, particularly regarding integration with primary care, which should be addressed alongside evaluations of long-term outcomes, healthcare use and costs and policy barriers in future studies.

Objective: Reliable and accurate measurement is fundamental to scientific progress; however, the dominant measurement practices in psychology, clinical psychology, and prediction research often lack rigor. Improving measures using Rasch Measurement Theory (RMT) offers advantages by fulfilling the key psychometric properties of unidimensionality, local independence of items, ordering of response categories, and invariance. Ordinal-level sum scores can be transformed into interval-level latent trait scores, thereby improving the measurement precision. However, the impact of using psychometrically advanced questionnaires with latent trait scores, as opposed to traditional sum scores, in predictive models is still unclear. This study evaluates whether using latent trait scores as predictors and outcomes, in accordance with RMT, improves predictive performance compared to using traditional sum scores when predicting treatment outcomes during psychological treatment.

Methods: Self-rated symptom data from three different questionnaires, collected over the first 4 weeks of psychological treatment from 6,464 patients undergoing a 12-week treatment program, were used to predict post-treatment outcomes on the same questionnaires. This was done in two ways: (1) using sum scores as the questionnaires were originally developed and (2) using a reformulated, more psychometrically robust version of the questionnaires based on Rasch analysis, which was also shorter. The prediction models used were linear regression, Bayesian ridge regression, and random forest. Multiple imputations were used to address missing data, and nested cross-validation was employed for hyperparameter tuning and scoring.

Results: Latent scores calculated using the psychometrically optimized shorter version, which comprises 23% of the full scale, showed similar predictive performance compared to the sum score of the full scale. Overall, there was a statistically significant but practically negligible difference of 0.007-0.008 in the root mean squared error (RMSE) when comparing the original sum score to the latent trait scores.

Conclusion: Initial findings comparing psychometrically improved questionnaires with the original ordinal sum scores within a predictive framework indicate that using latent trait scores derived from these improvements showed the predictive performance similar to the sum score of the full scale. The small differences suggest that the improved versions remain valuable owing to their enhanced psychometric qualities and the reduction in response burden by using considerably fewer items. Further research is needed to explore the use of latent trait scores compared to ordinal sum scores in predictive research.

Purpose: The aim of this study is to investigate the extent to which internal astigmatism compensates for corneal astigmatism in healthy young adults, focusing on the most common compensation patterns.

Methods: This cross-sectional study included 72 young adults aged 25–35 years. Four participants were excluded after quality control due to absence of corneal astigmatism. 68 right eyes were included in the analysis. All had healthy eyes and some degree of refractive error (46 myopic, 14 hyperopic, 8 emmetropic). Objective refraction and keratometry were obtained with the DNEye Scanner 2+. Internal astigmatism was calculated as the difference between refractive and corneal astigmatism and expressed as J₀and J₄₅ vectors. Compensation factor between corneal and internal astigmatism were computed and categorized. Aassociations were analysed using Pearson’s correlation test.

Results: A significant positive correlation was demonstrated between refractive and corneal astigmatism for both J₀ and J₄₅. A positive correlation was also noted between internal and refractive J₀, while no significant correlation was observed between internal and corneal J₄₅ (p >0.05). Undercompensation was the most frequent compensation pattern, whereas full compensation occurred in only about 3–4% of eyes.

Conclusion: A limited portion of corneal astigmatism is neutralized by internal astigmatism in young adults. Undercompensation was the most common form of compensation for both the J₀ (WTR/ATR) and J₄₅ vectors, while full compensation was uncommon. The total refractive astigmatism in these young adults is therefore dominated by the corneal component, since the internal astigmatism on average only partially balances the corneal astigmatism.

Can a quantum structure be directly identified in laughter? Here, we show that the basic structure of quantum logic can be identified in standup comedies and sketches. We analyzed scripts from standup comedies and sketches, employing a method to reveal the algebraic structures hidden within the semantic relations of the utterances. Our findings revealed that quantum logic is present in many instances of laughter, including punchlines. Moreover, we taught this method to ChatGPT, which corroborated our findings, indicating that the result is objectively significant. Our method and results, which directly address contextual relationships and identify quantum logic, are groundbreaking. We provide a novel methodology for quantum cognitive science and open new directions for research on sensations and emotions in Artificial Intelligence. Our method identifies contextual quantum-logical structure, but does not aim to model human-level general intelligence.

Electrospinning is a versatile technique for manufacturing micro-nano diameter fibers and has been used extensively for tissue engineering in vivo and advanced cell culture in vitro. Standard means of seeding cells onto such substrates typically offer no control over cell distribution, yielding dispersed, heterogeneous and low concentrations of cells. In this article, we investigate the viability of using a simple bioprinting-inspired device for seeding gel-encapsulated cells onto fiber substrates. Using human neural stem cells, we were able to consistently seed cells with spatial control. We examined their long-term development, showing viable cells and normal differentiation potential. Furthermore, this device was able to seed on multiple sites within a single substrate, creating isolated populations and demonstrating the potential for this approach as a low-cost alternative to bioprinting systems, which is also applicable to somewhat challenging 3D substrates like electrospun fibers.