This study examined age-specific associations between parent-reported executive function (EF) difficulties and internalizing/externalizing symptoms in children with neurofibromatosis type 1 (NF1), a genetic condition underrepresented in psychiatric research.

Cross-sectional data of 1,049 observations from 803 children with NF1 (Mage = 10.58 years, SD = 3.84, range = 3–18; 47.5% female; 67.5% from higher-education households; 36.6% with familial NF1) across nine U.S. and Australian institutions were integrated. Parents rated EF difficulties using the Behavior Rating Inventory of Executive Function and internalizing/externalizing symptoms using the Child Behavior Checklist and the Behavior Assessment System for Children. Time-varying effect modeling estimated age-specific associations between EF and internalizing/externalizing symptoms and tested moderation by sex and parental education.

Poorer functioning in all EF domains was associated with greater internalizing and externalizing symptoms from ages 3 to 18. The associations were largely consistent across ages as well as sex and parental education subgroups with a few differences: (1) emotional control and cognitive flexibility problems were more strongly associated with internalizing symptoms during middle and late adolescence; (2) inhibitory control was more strongly linked to externalizing symptoms in childhood; (3) stronger associations between EF and internalizing symptoms were observed among males in early adolescence.

Parental complaints of EF difficulties are robustly linked to internalizing and externalizing problems from early childhood to late adolescence in children with NF1. Further longitudinal and experimental studies are needed to determine the directionality of these associations and whether EF represents a viable target for intervention.

Digital mental health interventions have gained prominence as accessible and cost-effective solutions in workplace settings. However, our previous meta-analysis revealed a concerning trend: despite advancements in technology, the effectiveness of these interventions has not improved over time. This stagnation may be attributed to the significant and ongoing heterogeneity among interventions which indicates both variation in both sample and the intervention. We do not kow which theraputic approaches, design aspects or intervetnkion features enahnce efficacy.

We use a Bayesian meta-regression of an unpdated systematic review to develop a comprehensive framework to guide the design, development, and evaluation of workplace digital mental health interventions. By addressing the variability in intervention approaches and design and leveraging evidence-based practices, this framework seeks to enhance the quality and effectiveness of digital solutions for employee mental health.

A systematic literature review was conducted to identify randomized controlled trials of employee based digital mental health interventions. Eligible studies were assessed based on specific criteria, including participant characteristics, intervention characteristics, and outcome measures. Data extraction and coding were performed, followed by a Bayesian meta-analysis approach. This method allowed for a more nuanced evaluation of the effectiveness of various intervention features and designs, accounting for uncertainty and prior knowledge in the field.

The review identified 95 interventions involving approximately ˜25,000 participants. The Bayesian meta-analysis confirmed small positive effects in reducing mental ill-health symptoms. Both sample and intervention characteristics contributed to heterogeneity across studies. Stress management and mindfulness interventions, particularly those designed with input from mental health experts, demonstrated more effiacy than CBT based approaches. Several intervention features, such as videos, feedback scores, and reminder texts, were associated with positive mental health outcomes.

This review provides valuable insights into the optimal design and development of workplace digital mental health interventions. The identified framework and evidence-based practices offer guidance for developers to create effective interventions that address the heterogeneity within studies. Importantly, this framework has the potential to serve as a robust evidence base for app designers, enabling them to create more effective, personalised and engaging mental health applications.

None Declared

Electronic Health Record (EHR) data are critical for advancing translational research and AI technologies. The ENACT network offers access to structured EHR data across 57 CTSA hubs. However, substantial information is contained in clinical narratives, requiring natural language processing (NLP) for research. The ENACT NLP Working Group was formed to make NLP-derived clinical information accessible and queryable across the network.

We established the ENACT NLP Working Group with 13 sites selected based on criteria including clinical notes access, IT infrastructure, NLP expertise, and institutional support. We divided sites into five focus groups targeting clinical tasks within disease contexts. Each focus group consisted of two development sites and two validation sites. We extended the ENACT ontology to standardize NLP-derived data and conducted multisite evaluations using the Open Health Natural Language Processing (OHNLP) Toolkit.

The working group achieved 100% site retention and deployed NLP infrastructure across all sites. We developed and validated NLP algorithms for rare disease phenotyping, social determinants of health, opioid use disorder, sleep phenotyping, and delirium phenotyping. Performance varied across sites (F1 scores 0.53–0.96), highlighting data heterogeneity impacts. We extended the ENACT common data model and ontology to incorporate NLP-derived data while maintaining Shared Health Research Informatics NEtwork (SHRINE) compatibility.

This demonstrates feasibility of deploying NLP infrastructure across large, federated networks. The focus group approach proved more practical than general-purpose approaches. Key lessons include the challenge of data heterogeneity and importance of collaborative governance. This work also provides a foundation that other networks can build on to implement NLP capabilities for translational research.

Developing integrated mental health services focused on the needs of children and young people is a key policy goal in England. The THRIVE Framework and its implementation programme, i-THRIVE, are widely used in England. This study examines experiences of staff using i-THRIVE, estimates its effectiveness, and assesses how local system working relationships influence programme success.

This evaluation uses a quasi-experimental design (10 implementation and 10 comparison sites.) Measurements included staff surveys and assessment of ‘THRIVE-like’ features of each site. Additional site-level characteristics were collected from health system reports. The effect of i-THRIVE was evaluated using a four-group propensity-score-weighted difference-in-differences model; the moderating effect of system working relationships was evaluated with a difference-in-difference-in-differences model.

Implementation site staff were more likely to report using THRIVE and more knowledgeable of THRIVE principles than comparison site staff. The mean improvement of fidelity scores among i-THRIVE sites was 16.7, and 8.8 among comparison sites; the weighted model did not find a statistically significant difference. However, results show that strong working relationships in the local system significantly enhance the effectiveness of i-THRIVE. Sites with highly effective working relationships showed a notable improvement in ‘THRIVE-like’ features, with an average increase of 16.41 points (95% confidence interval: 1.69–31.13, P-value: 0.031) over comparison sites. Sites with ineffective working relationships did not benefit from i-THRIVE (−2.76, 95% confidence interval: − 18.25–12.73, P-value: 0.708).

The findings underscore the importance of working relationship effectiveness in the successful adoption and implementation of multi-agency health policies like i-THRIVE.

The population of adults with single-ventricle congenital heart disease (CHD) is growing. This study explores their lived experiences through an adult developmental psychology framework.

Individuals aged 18 and older with single-ventricle CHD participated in Experience Group sessions and 1:1 interviews. Sessions were transcribed and analysed thematically. Themes were categorized by developmental domains and age group.

Of the 29 participants, 18 (62%) were female, 10 (35%) were emerging (18–29 years), 13 (45%) were established (30–45 years), and 6 (21%) were midlife adults (46–60 years). Emerging adults expressed reluctance to initiate romantic relationships and fear of burdening partners, while established adults reported strong relationships with partners deeply involved in caregiving. Emerging adults struggled with finding fulfilling work that meets their health needs, whereas established and midlife adults faced unemployment or early retirement due to health limits. Family dynamics shifted, with established and midlife adults educating their children to become caregivers. Physical limitations and low self-rated health were consistent across life stages, and midlife adults did not worry about traditional chronic conditions. Mental health concerns, including anxiety and depression, persisted across all life stages, but resiliency and positive affect were also evident.

Adults with single-ventricle CHD experience developmental milestones differently, indicating the need for early anticipatory guidance in these domains to achieve optimal outcomes in adulthood.

There is emerging evidence of heterogeneity within treatment-resistance schizophrenia (TRS), with some people not responding to antipsychotic treatment from illness onset and a smaller group becoming treatment-resistant after an initial response period. It has been suggested that these groups have different aetiologies. Few studies have investigated socio-demographic and clinical differences between early and late onset of TRS.

This study aims to investigate socio-demographic and clinical correlates of late-onset of TRS.

Using data from the electronic health records of the South London and Maudsley, we identified a cohort of people with TRS. Regression analyses were conducted to identify correlates of the length of treatment to TRS. Analysed predictors include gender, age, ethnicity, positive symptoms severity, problems with activities of daily living, psychiatric comorbidities, involuntary hospitalisation and treatment with long-acting injectable antipsychotics.

We observed a continuum of the length of treatment until TRS presentation. Having severe hallucinations and delusions at treatment start was associated shorter duration of treatment until the presentation of TRS.

Our findings do not support a clear cut categorisation between early and late TRS, based on length of treatment until treatment resistance onset. More severe positive symptoms predict earlier onset of treatment resistance.

DFdF, GKS, EF and IR have received research funding from Janssen and H. Lundbeck A/S. RDH and HS have received research funding from Roche, Pfizer, Janssen and Lundbeck. SES is employed on a grant held by Cardiff University from Takeda Pharmaceutical Comp

To identify characteristics of US health systems and end users that report antimicrobial use and resistance (AUR) data, to determine how NHSN AUR data are used by hospitals and health systems and end users, and to identify barriers to AUR reporting.

An anonymous survey was sent to Society of Infectious Diseases Pharmacists (SIDP) and Society for Healthcare Epidemiology of America (SHEA) Research Network members.

Data were collected via Survey Monkey from January 21 to February 21, 2020. Respondent and hospital data were analyzed using descriptive statistics.

We received responses from 238 individuals across 43 US states. Respondents were primarily pharmacists (84%), from urban areas, (44%), from nonprofit medical centers (81%), and from hospitals with >250 beds (72%). Also, 62% reported data to the AU module and 19% reported data to the AR module. Use of software for local AU or AR tracking was associated with increased reporting to the AU module (19% vs 64%) and the AR module (2% vs 30%) (P < .001 each). Only 36% of those reporting data to the AU module used NHSN AUR data analysis tools regularly and only 9% reported data to the AR module regularly. Technical challenges and time and/or salary support were the most common barriers to AUR participation cited by all respondents. Among those not reporting AUR data, increased local expectations to report and better software solutions were the most commonly identified solutions to increase AUR reporting.

Efforts to increase AUR reporting should focus on software solutions and salary support for data-entry activities. Increasing expectations to report may incentivize local resource allocation to improve AUR reporting rates.

Globally, the corona virus disease 2019 (COVID-19) pandemic has created an interpersonally threatening context within which other people have become a source of possible threat. This study reports on the development and validation of a self-report measure of pandemic paranoia; that is, heightened levels of suspicion and mistrust towards others due to the COVID-19 pandemic.

An international consortium developed an initial set of 28 items for the Pandemic Paranoia Scale (PPS), which were completed by participants from the UK (n = 512), USA (n = 535), Germany (n = 516), Hong Kong (n = 454) and Australia (n = 502) using stratified quota sampling (for age, sex and educational attainment) through Qualtrics and translated for Germany and Hong Kong.

Exploratory factor analysis in the UK sample suggested a 25-item, three-factor solution (persecutory threat; paranoid conspiracy and interpersonal mistrust). Confirmatory factor analysis (CFA) on the remaining combined sample showed sufficient model fit in this independent set of data. Measurement invariance analyses suggested configural and metric invariance, but no scalar invariance across cultures/languages. A second-order factor CFA on the whole sample indicated that the three factors showed large loadings on a common second-order pandemic paranoia factor. Analyses also supported the test–retest reliability and internal and convergent validity.

The PPS offers an internationally validated and reliable method for assessing paranoia in the context of a pandemic. The PPS has the potential to enhance our understanding of the impact of the pandemic, the nature of paranoia and to assist in identifying and supporting people affected by pandemic-specific paranoia.