Background: Depression prevalence is rising in adolescents and young adults (ages 10-24 years). Traditional screening methods for depression, such as the Patient Health Questionnaire (PHQ-9), are particularly challenging for adolescents. AI has potential to help with this, but the scarcity of annotated datasets highlights the need for off-the-shelf approaches. In this study, we investigate the #feasibility of using state-of-the-art large language models (LLMs) to detect depressive symptoms in adolescent settings (ages 15-17 years). Objective: This study aimed to evaluate the #feasibility of using in-context learning with LLMs to identify depressive symptoms in free-text clinical notes for adolescent populations, to evaluate the #feasibility of off-the-shelf LLM configurations in detecting PHQ-9 symptom-related information under zero- and few-shot settings, and to demonstrate the potential clinical utility of LLM-driven evidence extraction through an interpretable AI-based depression screening tool. Methods: We examined free text of the electronic health records (EHRs) of adolescent patients with the diagnosis of depression or related mood disorders (age groups 15-17 years, 924 patients) from the Cincinnati Children’s Hospital Medical Center. We leveraged the combination of the Beck Depression Inventory (BDI) and the PHQ-9 to develop tailored categories suited for adolescent depression symptoms. We applied 3 state-of-the-art LLMs (Llama, smaller FLAN-T5, and Phi) to identify these symptom categories in zero- and few-shot configurations, allowing us to assess their performance without additional task-specific training. A word-search model was used as the baseline comparator. Results: We manually annotated clinical notes for 22 patients across 16 depression-related symptom categories. Our analysis shows that all LLMs substantially outperformed the word-matching baseline. Within the evaluated prompting configurations, FLAN-T5 achieved the highest precision (average F1-score=0.65, precision=0.78) and strong performance in detecting rare symptoms such as sleep problems (F1-score=0.92) and self-loathing (F1-score=0.80). Phi offered a balanced performance (precision=0.44, recall=0.60), while Llama 3 achieved the highest recall (0.90) but tended to overgeneralize, making it less suitable for precise screening. Within this pilot study, the best-performing LLM exceeded the baseline F1-score by 0.43. Finally, we demonstrate the clinical utility of FLAN-T5 annotations as features in a machine learning model, which distinguished depression cases from controls with high precision (0.78). Conclusions: We used a range of popular LLMs to identify indicators of adolescent (ages 15-17 years) depression symptoms in mental health notes. Within the evaluated configurations, FLAN-T5 demonstrated the most favorable balance between precision and recall. These results support the #feasibility of using off-the-shelf LLMs for symptom detection in adolescent clinical notes, while future work using standardized prompting strategies and task formulations will help establish more definitive comparisons between model architectures. The computational efficiency of FLAN-T5 further supports its potential for deployment in resource-limited clinical settings. Our findings require further validation for broader populations and other mental health conditions.

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Background: Major depressive disorder is a leading cause of disability, and access to specialist psychiatric follow-up remains limited in many settings. Telepsychiatry may reduce logistical barriers to care, but data on video-based follow-up under routine outpatient conditions in Türkiye are scarce, and comparisons of delivery modalities are frequently limited by baseline imbalance between self-selected groups. Objective: The aim of the study is to describe symptom trajectories and attendance over 6 months of routine psychiatric follow-up delivered either face-to-face or online and to examine whether outcomes differed between modalities after adjustment for baseline severity. Methods: In this prospective, nonrandomized observational cohort study, 120 adults with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) major depressive disorder were followed for 6 months at a university hospital psychiatry department in Ankara, Türkiye (September 2021 to May 2022). Participants entered face-to-face (n=60) or online (n=60) follow-up according to their own preference. Seven sessions were scheduled at approximately 4-week intervals; the Hamilton Depression Rating Scale (HAM-D; primary outcome), the Hamilton Anxiety Rating Scale, and DSM-5 Level 1 and Level 2 measures were administered at baseline and at sessions 1, 2, 4, and 6. Attendance was recorded at all sessions. Between-group comparisons used analysis of covariance adjusted for the corresponding baseline score and for prespecified covariates. Linear mixed models incorporating a group-by-time interaction were fitted to all available observations, and multiple imputation (m=20) was performed as a sensitivity analysis. Results: Depressive symptoms decreased substantially in both groups: mean HAM-D fell from 26.6 to 5.0 in the face-to-face group (81.2% reduction; Cohen d=4.86; P

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Depression fatigue isn't weakness or a willpower problem. Learn the biology behind the exhaustion, why rest doesn't fix it, and what treatment can help.

What If Depression Isn’t Just in Your Head? With Elite Guest Elyssa Schneider, Health Advocate, Educator & Speaker Together with Cece Shatz, Doyenne of Relationship Building | Going Solo Life Transitions What if understanding depression and emotional well-being requires looking beyond what's happening in the mind? Join Cece Shatz and Elite Guest Elyssa Schneider, Health Advocate, Educator & Speaker, for an eye-opening conversation exploring a broader perspective on depression, health, and overall wellness. Together, they discuss the importance of asking questions, becoming your own health advocate, understanding the mind-body connection, and exploring the factors that may influence how we feel. This conversation encourages viewers to become more informed and empowered when navigating their health and life transitions. Your emotional well-being is part of your whole health—and understanding yourself can be the first step toward positive change. A bit more about Cece... Cece Shatz is the “Doyenne” of Relationship Loss, Divorce, Transition into Dating, Dating, Single Parent’s Support and helping Singles learn to live their BEST lives.  She is an inspirational Divorce and Life Transition Coach, Public Speaker with a unique style of healing from within.  Her intuitive skills reach beyond to those who are struggling in coping and redefining their lives.  Cece has relate-ability as she has the “been there done that” approach to life.  She has fondly been called the Life Changing Coach - Your Best Friend .... Your Mentor! Connect with Cece: goingsolonetwork@gmail.com www.goingsolonetwork.com www.goingsolodirectory.com www.NEWStreamingNetwork.com.  Home of NEW Streaming TV, Going Bold Media, Going Solo Media. and WGSN-DB Going Solo Network Radio, Podcasts, and TV. More about Elyssa: Elyssa Schneider is a health advocate and educator focused on raising awareness about the connection between environmental toxicity and mental health. Her work grew out of her own 28-year struggle with depression and her experience addressing mercury and lead exposure as part of her personal health journey. After seeing profound changes in her own life, Elyssa began supporting others who were searching for answers to persistent symptoms and witnessed meaningful transformations as they explored and addressed heavy metal exposure.  Today, she helps individuals and families become more informed, ask better questions, navigate available testing and professional resources, and feel less alone in the process. Elyssa combines personal experience, ongoing education, holistic health principles, and a faith-based perspective to encourage people to take a more proactive role in understanding their health. My story: https://youtu.be/VJ0HPt7AN48?is=Se8eiy5YpvLQV4E2 LinkedIn: https://www.linkedin.com/in/elyssa-schneider-2681a1278?utm_source=share_via&utm_content=profile&utm_medium=member_ios Facebook: https://www.facebook.com/share/1KCdJcw4qP/?mibextid=wwXIfr Youtube: elyssaschneiderspeaks Instagram: @elyssa.schneider.speaks