Background: Health care workers continue to experience heightened levels of distress and burnout, which contribute to higher levels of job dissatisfaction, turnover intentions, presenteeism, and staffing shortages. Objective: The aim of this study was to examine how participation in the StressPal Frontline: Essential Resilience Self-Care and Burnout Prevention program influenced health care workers’ stress and resilience. The study also sought to identify specific measures of perceived stress and resilience that were most affected by participation in the program and to explore whether pre-and-post differences varied based on participant characteristics. The StressPal Frontline program is a digital resilience intervention specifically developed for health care workers to enhance psychological flexibility and stress resilience. The self-paced training program, designed for approximately a 6-week period, consists of brief modules, follow-up resources, and a peer engagement community. Methods: A pretest-posttest quasi-experimental design was used to assess the effectiveness of the StressPal Frontline program in reducing stress and building resilience among 76 health care workers who voluntarily joined and completed the program. Outcome measures included the Perceived Stress Scale and the Brief Resilience Scale to assess participants’ perceptions of stressful situations and their ability to bounce back from stress. Descriptive statistics, correlation analysis, paired-samples 2-tailed test, and multiple regression analysis were conducted. The paired-samples test was calculated at the scale level and item level to evaluate the statistical significance of pretest and posttest mean differences, and the Cohen statistic was used as a measure of effect size. Statistical analysis for this study was conducted in Excel (Microsoft), SPSS (IBM Corp), and Jamovi (jamovi project). Results: The results indicated a 1.53-point reduction in the Perceived Stress Scale score after participating in the StressPal Frontline program, suggesting a statistically significant decline in average perceived stress due to participation in the program (=.004). The corresponding value of Cohen was 0.34, suggesting a small-to-medium effect of the intervention, StressPal Frontline program, in reducing perceived stress. For the Brief Resilience Scale, pre-and-post difference was not significant at the scale level (=.07); however, item-level analysis found significant increases in participants’ perception of their ability to bounce back quickly after hard times and handle difficult situations. No significant differences were found in outcome measures based on age, race, ethnicity, professional role, or practice setting. Conclusions: The StressPal Frontline program was associated with positive outcomes in reducing perceived stress. Our study also found no statistical differences in outcomes among participants of different age groups, races, ethnicities, occupations, genders, and practice settings. This is an important finding, as it indicates that the StressPal Frontline program may provide positive benefits for reducing stress across professions, settings, and individual characteristics. This program, along with other resources, could be implemented by health care organizations to support workers’ professional development, behavioral health, and well-being.
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Virtual Patients Will Train Future Mental Health Clinicians
Researchers from the University of Pennsylvania and New York University have received a $4 million grant from the Wellcome Trust to develop an AI-driven platform to train mental health clinicians using simulations of real patients.
Within the next two years, the partners will work on the development of the STELLAR platform, which stands for Steering-Vector Enhanced LLM Agents for Realistic Digital Twins in Mental Health. The platform will create digital twins of patients that trainees can use to practice conducting clinical interviews and evaluating psychiatric symptoms.
“STELLAR brings together behavioral data, clinical expertise, and AI to ask a very practical question,” said Sharath Chandra Guntuku, PhD, associate professor of computer and information science at Penn Engineering. “Can we build training tools that better prepare clinicians for how varied and complex patients are?”
Preparing future mental health clinicians for clinical interviews can be challenging as patients will often report overlapping symptoms that shift over time and subjective experiences that can be expressed differently by each individual. STELLAR will give trainees an ethical option for trainees to simulate interviewing patients with a broad range of symptoms, backgrounds, and clinical scenarios.
“In psychiatry, the details of symptom experience matter: how someone describes distress, how symptoms overlap, how severity changes over time, and how context shapes the clinical interaction,” said Raquel E. Gur, MD, PhD, professor of psychiatry, neurology, and radiology at Penn’s Perelman School of Medicine.
Patient simulations will be created drawing from clinical data from the Philadelphia Neurodevelopmental Cohort, a repository including psychiatric assessments and clinical interviews from thousands of young people created by Penn Medicine and the Children’s Hospital of Philadelphia. Rather than copying individual patients, the simulations will create composites based on real-world data for clinicians to practice realistic conversations in the context of a clinical interview.
This will allow trainers to precisely control the symptoms students encounter, their intensity, and how they interact with each other. For instance, a trainee may practice interviewing a patient with mild anxiety and another whose anxiety overlaps with depression or psychosis to learn how to distinguish the differences in presentation between both.
Because many mental health symptoms manifest beyond formal clinical settings, the platform will also be trained using data from social media platforms, where people discuss mental health symptoms in everyday language.
“Patient simulations will only be useful for clinician training if they are grounded in real clinical speech and evaluated as clinical interactions, not just plausible AI dialogue,” said Neville Ryant, PhD, researcher at Penn’s Linguistic Data Consortium. “[Our] role is to bring speech and language science into the core of the project: adapting speech-recognition tools to clinical interviews, creating high-quality transcripts and annotations, and helping evaluate both what the simulations say and how they say it. That includes assessing the language generated by the models, the naturalness of synthetic voices, how well those voices reflect target speech patterns, and the behavior of the avatar during real trainee interactions.”
To ensure the conversations are realistic, respectful, and useful to trainees, the team will involve people with lived experience of mental health conditions as well as family members and caregivers to provide their perspective into the evaluation process. Their feedback will help researchers assess the accuracy of simulations, avoid stereotyping patients, and prepare trainees for complex and nuanced clinical conversations with real patients.
“The promise of this approach is that we can move beyond stylized and potentially biased simulations,” said João Sedoc, PhD, assistant professor of technology, operations and statistics at NYU’s Stern School of Business. “If we can create digital patients that simulate controllable plausible symptom expression and responsibly evaluate, we can augment current clinician training practices with the kinds of conversations that are essential to better mental health care.”
The post Virtual Patients Will Train Future Mental Health Clinicians appeared first on Inside Precision Medicine.
Astrocytes Preserve Memory Persistence Through Ankyrin-2 Protein in Mice
Although scientists have long studied how memories are formed in the brain, how certain memories persist over time for learning and cognitive function remains unclear.
A new study published in Nature Communications titled, “Astrocytic ankyrin-2 enables memory persistence in the mouse hippocampus,” suggests that astrocytes play a critical role in long-term memory through the regulatory protein ankyrin-2 (Ank2).
Removing Ank2 function led to significantly impaired memory in mice after after two weeks. Under normal conditions, these mice showed standard locomotion, sociability, and recent memory immediately after learning.
Astrocytes lacking Ank2 formed significantly less physical contacts with nearby engram neurons, the specialized neurons for memory storage. Additionally, the maintenance of long-term potentiation (LTP) was impaired while normal synaptic transmission remained intact. The findings suggest that astrocytes stabilize the neural circuits required for preserving memories long after they are formed.
On the molecular level, researchers found that Ank2 is required for brain-derived neurotrophic factor (BDNF) signaling through the astrocytic TrkB.T1 receptor and IP3R2-mediated calcium signaling. In the absence of Ank2, calcium signaling weakened, astrocytes failed to undergo normal structural remodeling, and showed reduced ability to maintain contacts with memory-encoding neurons.
The researchers further demonstrated that hippocampal BDNF infusion normally strengthens long-term memory persistence, but this effect disappeared when astrocytic Ank2 was deleted, showing that Ank2 is essential for BDNF-dependent memory stabilization.
To determine whether astrocytic BDNF signaling alone is sufficient to enhance memory, the team developed an optogenetic tool called Opto-T1. Activation of this pathway promoted astrocyte remodeling, maintained long-term potentiation, and significantly enhanced remote memory without affecting recent memory.
“Our findings show that astrocytes are not passive support cells, but active regulators that determine how long memories last,” said Wuhyun Koh, PhD, senior research fellow at Institute for Basic Science (IBS) and corresponding author of the study. “By identifying Ank2 as a key regulator of astrocyte remodeling and BDNF signaling, we have uncovered a new mechanism that helps stabilize long-term memories and opens new avenues for understanding and potentially treating memory disorders.”
The researchers indicate the study provides a new framework for understanding how astrocytes contribute to neurological diseases.
The post Astrocytes Preserve Memory Persistence Through Ankyrin-2 Protein in Mice appeared first on GEN – Genetic Engineering and Biotechnology News.
Millions of People in Canada Are Finding AI-Enabled Support for Mental Health Effective Amid Ongoing Questions Around Trust.
(OTTAWA) July 8, 2026 — New polling shows approximately six million people in Canada used AI-enabled tools for mental health support in the past year and most find them effective. Today, the Mental Health Commission of Canada (the Commission), in partnership with Mental Health Research Canada (MHRC) and Pollara Strategic Insights, releases the first nationally representative data on how people in Canada engage with digitally supported mental health tools, including AI and virtual care, across every province and demographic.
Quick Facts:
- 1 in 7 people in Canada used AI mental health tools in the past year
- Three out of four who used AI and virtual mental health services found them effective for their well-being
- Only 14 % trust AI tools, just 2% trust them completely
- 40 % of AI users said they were more likely to seek professional care
- Nearly half (45%) who accessed mental health care did so virtually, in whole or in part
WHY IT MATTERS
People in Canada are turning to AI as a convenient way to access mental health support. AI-enabled tools may offer greater convenience and accessibility. Among those surveyed, AI is being used because it is:
- Free or low-cost; 46% of AI users cite this as the reason they use it during a time when financial stress is itself a cause for anxiety.
- Always available; 44% of AI users cite 24/7 access.
- Immediate and convenient; it can be used from anywhere without travelling or waiting for an appointment. For someone in rural Canada, it saves time and travel costs.
- Seemingly private; 39 % of AI service users cite private, anonymous support as a reason for use, while privacy and data protection remain key public concerns.
AI is most used for general well-being (42%), companionship (36%), and mild-to-moderate stress (36%), and 40% of AI users said they were more likely to seek professional care.
WHO IS USING IT AND HOW MUCH DO THEY TRUST IT?
Use is higher among people in Canada under 35 (27%; 29% among men aged 25–34), newcomers to Canada (28%), racialized people in Canada (23%), and 2SLGBTQI+ communities (20%), populations that may experience greater barriers to traditional care.
Overall, trust remains low, particularly for AI-enabled tools, where only 2% of people in Canada trust them completely. People in Canada over 55 show the lowest adoption and trust.
VIRTUAL CARE: EFFECTIVE AND MORE TRUSTED BUT FALLS SHORT OF IN-PERSON SERVICES
45% of people in Canada who used mental health services in the past year did so virtually, with 75% reporting positive outcomes. However, nearly 1 in 3 prefer a hybrid model that combines virtual and in-person services. The data signals what people in Canada need: well-designed tools for safer digital mental health care that they can trust.
THE COMMISSION OFFERS GUIDANCE FOR THE DIGITAL MENTAL HEALTH ERA
The Commission is Canada’s trusted resource for safe digital mental health — assessing apps and tools, setting evidence-based standards, and leading the national conversation on guidance for AI in mental health and substance use health care.
As virtual services and AI-enabled tools continue to expand rapidly across the mental health landscape, there is a growing need for evidence-based insight into how people in Canada engage with, understand, and perceive them. The Commission partnered with MHRC to leverage their ongoing national polling initiative and provide timely insights into usage, attitudes, and concerns related to e-mental health and AI.
The polling is clear: people in Canada want to close the gap between availability and trust. The Commission is working with the Canadian Centre on Substance Use and Addiction and collaborators, provincial governments, technology developers, and health system partners to establish guidance for AI.
“Six million people in Canada have already used AI for mental health support and most found it convenient and effective for their well-being. It is critical that AI is safe and equitable to increase public trust and reduce harms.” – Lili-Anna Pereša, President and Chief Executive Officer, Mental Health Commission of Canada
“The people turning to digitally-supported mental health tools are often those facing some of the greatest barriers to care. Making sure these tools are safe, effective, evidence-based and human-centred is a matter of equity. Ongoing research is essential to understanding where they help and where safeguards are needed.”– Akela Peoples, Chief Executive Officer, Mental Health Research Canada
About Mental Health Commission of Canada
As an independent, not-for-profit with charitable status, the Commission collaborates with leading experts and organizations nationally and internationally, including with people with lived and living experience, to develop national guidelines, standards and strategies, promote innovation and best practices, reduce stigma, increase mental health literacy, and support all levels of government to improve mental health outcomes for everyone living in Canada. The Commission is Canada’s trusted resource for digital mental health best practices with the e-Mental Health Strategy for Canada, app assessment, e-modules for e-mental health implementation, and AI guidance for mental health and substance use health.
About Mental Health Research Canada
As an independent national charity, MHRC works hard to enable a future where mental health in Canada is transformed using evidence, data and stakeholder engagement. We unite researchers, communities, and people with lived experience to bridge gaps in care through national population polling, rapid data reporting, and partnerships that inform policy to improve outcomes. Learn more at www.mhrc.ca
About the Polling
Conducted by Pollara Strategic Insights in partnership with Mental Health Research Canada and the Mental Health Commission of Canada, this national poll (n=3,519) is the first representative data on AI use for mental health in Canada. Full findings: https://mentalhealthcommission.ca/AI-polling-report
About the Funding
The views in this report solely represent the views of the Mental Health Commission of Canada. Production of this report is made possible through financial contribution from Health Canada.
Media Contact
Heather Bakken, Pendulum Group
email: heather@pendulumgroup.ca
cell: 613-406-5432
The post Millions of People in Canada Are Finding AI-Enabled Support for Mental Health Effective Amid Ongoing Questions Around Trust. appeared first on Mental Health Commission of Canada.
Birmingham trusts launch InterSystems shared patient portal
Birmingham and Solihull Mental Health and Birmingham Community Healthcare NHS Foundation Trusts have launched a shared patient portal.
Loneliness From the Digital Mental Health Practitioners’ Perspective: Thematic Analysis of Semistructured Interviews
Background: Loneliness is a prevalent concern across the United Kingdom. While validated scales exist to quantify the severity of loneliness across populations, there remains a gap in understanding how loneliness manifests and is addressed within therapeutic practice. Given the associated stigma surrounding loneliness, practitioner perspectives offer crucial insights into how clients express loneliness within digital therapeutic environments. These insights can inform more nuanced conceptualizations of loneliness. Objective: This study aimed to gather the practitioners’ perspectives on loneliness within a digital therapeutic context and were defined as follows: (1) understand how practitioners identify loneliness concerns, (2) identify how loneliness is elicited in digital mental health interventions, and (3) identify co-occurring themes (such as grief, shame, and social disconnection) that signal loneliness concerns in client communications within digital therapeutic environments. Methods: Semistructured interviews were conducted with 9 practitioners. Participants included specialists in grief counseling, lesbian, gay, bisexual, transgender, and queer or questioning plus support; and digital mental health therapists. Interview transcripts were analyzed using thematic analysis, using an inductive, data-driven approach to allow themes to emerge from participant accounts rather than fitting data to preexisting theoretical frameworks. Results: The following four themes were identified: (1) Conceptualizing Loneliness: practitioners distinguished between social contact and meaningful connection; (2) Contextual Causes: loneliness emerged from life transitions, stigmatized identities, and resource reduction (eg, youth services closures and social support); (3) Expressions and Language: clients rarely expressed loneliness directly, instead using proxy terms, with disclosure patterns varying by age; and (4) Mental Health Co-occurrence: severe mental health conditions created bidirectional cycles of loneliness, exacerbated by symptoms of mental health difficulties. Practitioners reported that many clients experienced loneliness concerns, yet direct disclosure was absent across all participants’ experiences. Conclusions: Practitioners identified multiple stigmatizing experiences as contextual drivers of loneliness, particularly demonstrating how loneliness emerges not only from individual experiences but from broader patterns of social exclusion and marginalization. For therapeutic practice, these insights suggest that practitioners can use awareness of stigmatizing experiences as potential indicators when assessing loneliness risk. The presence of contextual patterns was consistent across practitioners’ experiences, providing a foundation for developing more targeted interventions to address both the emotional experience of loneliness and the underlying social drivers.
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AI Reveals Hidden Brain Lesions in Multiple Sclerosis MRI
It has long been known that brain gray matter plays a key role in multiple sclerosis (MS) disease progression and cognitive impairment, but because magnetic resonance imaging (MRI) has only been able to detect lesions in white matter, neither clinicians nor researchers have had a way to detect or monitor gray matter (cortical) lesions. And while many new drugs developed in the past decade can slow disease progression significantly, they primarily work on reducing white matter lesions.
A University at Buffalo (UB)-led team now reports that it has found a way to use artificial intelligence to reveal these otherwise invisible cortical lesions by reviewing existing MRI scans. The researchers say the significance of finally being able to see what has been known as one of the most important indicators in MS disease progression cannot be overstated.
“Detecting previously invisible cortical lesions on conventional legacy MRI scans has major implications for MS research and clinical care,” commented Robert Zivadinov, MD, PhD, SUNY distinguished professor in the Department of Neurology and director of the Buffalo Neuroimaging Analysis Center (BNAC) in the Jacobs School of Medicine and Biomedical Sciences at UB. “The ability to see for the first time these previously hidden indicators of MS disease progression, including cognitive impairment and disability, is an important advance.”
Added Michael G. Dwyer, PhD, associate professor of neurology and biomedical informatics in the Jacobs School and a researcher with BNAC, “What this collaboration has been able to accomplish is a real success story for applying AI in the medical arena. We now have access to these incredibly useful data on MRI scans that were there but you couldn’t see them without using AI to pull them out. The computational methods are finally at the point where we can do this.”
Zivadinov is senior author, Dwyer first and corresponding author of the team’s published paper in Communications Medicine, titled “Quantifying cortical lesions in multiple sclerosis MRI datasets using multi-contrast post- processing and deep learning.”
“Multiple sclerosis (MS) affects both the inner, connectivity-oriented portions of the brain (white matter) and the outer layer of the brain (the cortex),” the authors explained. While the involvement of cortical lesions in MS has been known almost since the identification of MS in the late 19th century, they weren’t included on diagnostic criteria until the 21st century. And even when they were included, it was noted that their use would be greatly limited due to the current capabilities of clinical MRI.
“Historically, research and clinical care in MS have focused on white matter, where focal demyelinating lesions are a hallmark of the disease,” they continued. And although there are now many therapies that can almost completely halt the incidence of new white-matter lesions in individuals with MS, they haven’t had the same impact on clinical progression, the team continued.
Over more recent decades it’s been found that gray matter is affected from the earliest MS disease stages, and it’s become evident that gray matter pathology is more than secondary to white matter damage. “From a clinical perspective, cortical lesions are strongly associated with clinical disability and cognitive impairment,” the authors stated. “They may also have more prognostic value than white matter lesions for disability and disease course.”
There’s an urgent need for in vivo imaging methods that can show gray matter lesions, they stressed. Dwyer added, “We have all been very frustrated, knowing that these cortical lesions were there but not being able to see them. There’s a lot of ongoing damage that continues to happen in MS that you won’t see with conventional MRI, but that histopathologists have been clearly demonstrating for decades on postmortem tissue.”
For their newly reported study the team applied advanced image processing techniques, including artificial intelligence, to standard MRI scans from a large MS clinical trial. “Recently, several post-processing methods, including synthetic contrasts and artificial intelligence (AI)-based approaches, have shown potential for enhancing cortical lesion detection on conventional MRI data,” they noted. “These methods have the potential to reanalyze existing clinical-trial data to answer key mechanistic questions about both MS development and about treatment effects.”
The AI approaches the researchers used, building on work from co-authors from the Netherlands, were designed to extrapolate vital information from the relationships between multiple images that can’t be seen on a single image.
The researchers combined multiple image-processing techniques, including a new one they developed called MMCLE, or multimodal cortical lesion enhancement. They then applied these techniques to MRI scans from the large, phase III FDA regulatory ORATORIO clinical trial, a study of the MS drug Ocrelizumab that included more than 700 participants.
They found that while individual images of a patient’s brain revealed mostly white matter lesions, once they applied the AI-based image processing methods to multiple different contrast images, they were able to see anywhere from 15 to 20 cortical lesions for each patient, more than 11,000 for the whole dataset. “We confirmed that cortical lesions can be clearly visualized and quantified with these methods,” they stated. “Using deep learning, we also confirmed that the simultaneous use of multiple contrasts improves quantification.”
Dwyer explained further, “If you look on the original scans, you generally can’t see the cortical lesions, but generative AI is very powerful because it can look between the scans and detect tiny differences between them. Because it sees those minor discrepancies, AI can reveal that there’s something going wrong there, that the tissue is not behaving like healthy tissue. The trained models can view multiple MRI images together and synthesize them and synthesize what had been missing.”
Zivadinov added “This work, which has revealed that there is so much invisible pathology in the brain, will have tremendous impact for reviewing data from past clinical trials and also for those going forward,” he says.
The post AI Reveals Hidden Brain Lesions in Multiple Sclerosis MRI appeared first on GEN – Genetic Engineering and Biotechnology News.
Hair cortisol as psychotherapy process parameter – an inpatient pediatric psychosomatic study
IntroductionPediatric-psychosomatic inpatient therapy is an essential part of the German health care system for the treatment of mental disorders in children and adolescents. However, empirical research in this field remains scarce and limited to psychological parameters. This longitudinal naturalistic study aimed to evaluate the efficacy and sustainability of inpatient psychosomatic therapy in children and adolescents by examining both psychological outcomes and biological markers.MethodsA total of 58 patients were assessed at seven time points before, during, and after treatment. Hair cortisol concentration (HCC) was measured as a neuroendocrine parameter of long-term stress regulation. Psychometric data were collected using five validated questionnaires.ResultsFindings indicated significant improvements in perceived stress, depressive and anxiety symptoms, family functioning and internalizing symptoms in the course of inpatient treatment. Overall, these effects remained stable at three- and six-month follow-ups, with only transient increases in depressive symptoms and family problems. HCC showed a significant decrease from admission to discharge and remained stable across follow-ups.DiscussionThese results support the efficacy of inpatient pediatric psychosomatic interventions on both psychological outcomes and neuroendocrine stress regulation and highlight the value of integrating biological markers into psychotherapy research.
Mental health stigma among nursing students: current status and intervention strategies— an integrative review
ObjectiveAims to synthesize the current evidence on the prevalence of mental health stigma among nursing students and to summarize the interventions that have been developed and evaluated to reduce such stigma.MethodsA comprehensive search was conducted across PubMed, CINAHL, and Web of Science databases for peer-reviewed studies published in English between January 2018 and June 2024.ResultsA total of 25 studies were included. The prevalence of mental health stigma among nursing students was found to be moderate to high. Stigma manifested in various forms, including negative attitudes toward patients, internalized shame, and public stigma directed at nursing students themselves. Interventions such as mental health training, empathy enhancement programs, and clinical internships demonstrated varying degrees of effectiveness in reducing stigma, with multi-component interventions showing the most promise.ConclusionMental health stigma among nursing students is a persistent and multifaceted issue that requires targeted strategies to address. Enhancing mental health education, increasing clinical exposure, and implementing comprehensive intervention programs are essential steps to reduce stigma and support the development of compassionate, skilled nursing professionals.
The race for an Ebola therapy begins
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Hope you got to relax this weekend. Here’s my favorite July Fourth video. (It’s loud.)