<![CDATA[In this inaugural episode, experts discuss the limitations of current diagnosis-based frameworks in suicide research, emphasizing the need for more targeted interventions. ]]>
<![CDATA[In the first ever episode of “Psychopharm Today,” experts unpack why suicide needs its own research, how to design targeted studies, and what clinicians can do beyond diagnosis to reduce risk.]]>
Neural and autonomic regulation during brief mindfulness and relaxation interventions in clinical populations: a multimodal MEG study protocol
Mental disorders pose a major and growing challenge for health care systems worldwide, marked by persistent treatment gaps and limited access to psychotherapeutic care. Mind–body interventions such as mindfulness and relaxation practices are widely used in clinical contexts as low-threshold strategies to support stress regulation and psychological well-being. Despite their broad application, the mechanisms underlying their acute effects remain insufficiently understood, particularly regarding brain–body interactions. This study protocol describes a prospective multimodal investigation of regulation during brief mindfulness-and relaxation-based interventions in clinical populations (at least n = 15 adults with depression according to SCID-5-CV and at least n = 15 adults with adult ADHD according to SCID-5-CV). Using a standardized within-subject experimental paradigm, magnetoencephalography (MEG) will be combined with electrocardiography (ECG) and respiratory measures to capture fast neural dynamics and autonomic regulation during three randomized auditory conditions: mindfulness (body scan), relaxation (safe place imagery), and an auditory control condition (podcast). Subjective ratings of stress and relaxation will be collected repeatedly across the procedure, complemented by questionnaires characterizing interindividual differences relevant to regulation. Outcome measures will include indices of autonomic regulation derived from cardiac activity and respiration, as well as repeated subjective ratings of stress and relaxation across conditions. Neural measures (MEG) will be used to characterize condition-related brain dynamics and brain–body coupling metrics linking neural oscillations to cardiac and respiratory rhythms. Speech-related measures during auditory guidance and brief speech-production features from post-condition reflections will be included as complementary and exploratory extensions to increase psychotherapy relevance, while accounting for methodological challenges related to overt speech in MEG. By integrating neural, physiological, and subjective measures within a single standardized paradigm, this study protocol aims to advance a mechanistic understanding of brief mind–body interventions in clinically relevant populations. Focusing on dynamic brain– body interactions during stress and regulation, the proposed approach is designed to support transparent and reproducible investigation of regulatory processes that are relevant to psychotherapy-related mind–body approaches, clinical practice, and everyday self-regulation. The findings are expected to inform future translational research and contribute to the development of mechanism-informed and potentially personalized applications of mindfulness-and relaxation-based interventions.Study protocol registrationPreregistration can be found here: https://osf.io/3rhk4/overview.
Perceived organizational climate and turnover intention among young nurses from a humanistic care perspective: the mediating role of work engagement
BackgroundThe high turnover of young nurses poses a significant challenge to the stability of healthcare systems worldwide. While the relationships between perceived organizational climate, work engagement, and turnover intention are established, there is a lack of research integrating a humanistic care management perspective to elucidate the specific psychological mechanisms among young nurses at their uniquely vulnerable career stage. Drawing upon this contextual lens, this study aims to evaluate the status of perceived organizational climate, work engagement, and turnover intention among young nurses, and to further explore the potential mediating role of work engagement in the relationship between perceived organizational climate and turnover intention.MethodsA cross-sectional study was conducted from July to September 2025, surveying 366 young nurses from a tertiary Grade-A hospital in Shaanxi Province, China, using convenience sampling. Data were collected using a general information questionnaire, the Utrecht Work Engagement Scale, the Organizational Climate Questionnaire, and the Turnover Intention Questionnaire. A Structural Equation Model was employed to analyze the mediating effect.ResultsThe findings revealed significant interrelationships among perceived organizational climate, work engagement, and turnover intention. Work engagement was found to partially mediate the relationship between perceived organizational climate and turnover intention during young nurses, explaining 34.78% of the variance.ConclusionThese findings suggest that organizational climate functions as a critical job resource that may buffer turnover intention by fostering higher levels of work engagement. To maintain workforce stability, nursing managers should integrate humanistic care into organizational policies to cultivate a supportive environment. However, due to the cross-sectional design and convenience sampling from a single institution, causal inferences should be made with caution, and the generalizability of the findings may be limited.
Cultural adaptation and feasibility of action over inertia in Japan: a multi-site pilot intervention study
IntroductionAction Over Inertia (AOI) is a recovery-oriented intervention designed to promote occupational engagement and support personal recovery through changes in everyday activity patterns. While AOI has been examined in Western contexts, its applicability and clinical adaptation in East Asian settings remain underexplored. This study used a mixed-methods approach to examine the feasibility and cultural adaptation of a group-based AOI program in Japan.MethodsA mixed-methods pilot feasibility design was used. The quantitative component was a single-arm repeated-measures pilot study conducted across four psychiatric daycare facilities in Japan. Outcomes were assessed at baseline, post-intervention, and 1-month follow-up in 12 participants with serious mental illness using the Recovery Assessment Scale (RAS), Temple University Community Participation Measure-Japanese version (TUCP-J), and Social Functioning Scale (SFS), alongside symptom severity assessed with the Brief Psychiatric Rating Scale (BPRS). These measures were used as exploratory clinical outcomes to describe preliminary patterns of change and to inform future controlled studies, rather than to determine intervention effectiveness. The qualitative component involved semi-structured interviews with occupational therapists who delivered the intervention, to contextualize the quantitative findings and identify implementation barriers and adaptations. The quantitative intervention study was approved by the Research Ethics Committee of the Graduate School of Medicine, Nagoya University, Japan (2023-0209), and registered with the UMIN Clinical Trials Registry (UMIN000045392). The qualitative interview study was approved by the Ethics Review Committee for Research Involving Human Participants, Nihon Fukushi University, Japan (23-041-03).ResultsNo significant improvements were observed in the exploratory outcomes of recovery, participation, or social functioning. Although BPRS showed a significant overall time effect, post-hoc comparisons between time points were not significant, and this change cannot be causally attributed to AOI because of the single-arm design. In an exploratory observation, participants with higher symptom severity appeared to show lower community participation. Qualitative findings suggested that the intervention dose, participant–program fit, readiness for activity change, and group-based delivery conditions may need further optimization within Japanese psychiatric daycare settings.DiscussionThe findings suggest that group-based AOI may be feasible to implement in Japanese psychiatric daycare settings, but further cultural and clinical adaptation is needed before effectiveness can be evaluated in controlled trials. Adaptation may require preserving AOI’s theoretical foundations while making the intervention less exposing, less abstract, and more accessible in group-based settings. Future studies should use controlled designs, longer follow-up periods, and broader outcome indicators to examine the effectiveness and implementation of culturally adapted AOI.Clinical trial registrationUMIN Clinical Trials Registry https://www.umin.ac.jp/ctr/, identifier UMIN000045392.
Social anxiety but not callous-unemotional traits predicts shame coping in conduct disorder
IntroductionConduct disorders are characterized by emotional dysregulation. Both callous-unemotional traits and social anxiety are heightened in conduct disorder patients and are associated with different mechanisms of emotion regulation. Previous evidence has proposed that secondary emotions, such as shame, might also be affected in conduct disorder and that callous-unemotional traits and social anxiety might be related to shame as well as to shame coping. Therefore, the current study investigated links between callous-unemotional traits, social anxiety, shame proneness, and shame coping in adolescent inpatients with conduct disorder.MethodsForty adolescent inpatients with conduct disorders (M = 12.4, SD = 1.4) filled in questionnaires on callous-unemotional traits, social anxiety, shame proneness, and shame coping. Correlational and regression analyses, as well as mediation analyses were performed.ResultsCallous-unemotional traits were not associated with any other construct. Social anxiety showed positive correlations with shame proneness and internalizing as well as externalizing shame coping. Social anxiety was also a significant predictor of internalizing shame coping while controlling for shame proneness and callous-unemotional traits. No predictors emerged for externalizing shame coping. Mediation analyses confirmed that neither shame proneness nor social anxiety mediated the relationship between CU traits and shame coping, as CU traits were not significantly associated with either variable.DiscussionThe findings suggest that social anxiety plays a key role in internalizing shame coping in conduct disorder patients. CU traits appear to be unrelated to shame proneness and shame coping, either directly or indirectly, in conduct disorder.
Evaluating Postpartum Hemorrhage Transfusion Risk With a Machine Learning Model for Informed Consent: Retrospective Cohort Study
<strong>Background:</strong> Postpartum hemorrhage requiring a blood transfusion is a concern for patients and clinicians; its risk and the mode of delivery are important points of discussion before labor. Many high-risk factors associated with postpartum hemorrhage are known prior to delivery and are often unpreventable. Delivery plans are influenced by the patient’s medical history, their preferences, and clinical decision-making. Informed consent regarding known risk factors for postpartum hemorrhage will help guide delivery care plans and mitigate risk. Machine learning models have been used to predict postpartum hemorrhage; however, translation into clinical support tools is challenging. Shared decision-making discussions can be facilitated with machine learning model–based clinical support tools predicting postpartum hemorrhage requiring a transfusion. <strong>Objective:</strong> We sought to develop a machine learning model for prediction of postpartum hemorrhage requiring a transfusion. Specifically, we sought to evaluate the model’s accuracy in predicting a patient’s postpartum transfusion risk based on delivery mode, whether labor was induced, and the delivery indications for the purpose of antenatal clinical decision support. Model performance was evaluated on existing structured data and physician-reviewed datasets. <strong>Methods:</strong> A 10-year retrospective cohort of 62,521 births in a community health system was sampled. A convenience sample of 1734 patients was analyzed to predict blood transfusion rate based on delivery mode and delivery indications. XGBoost, random forest, and generalized linear models were trained and compared for performance. Datasets were evaluated using the best-performing XGBoost machine learning model. A prototype clinical support app for physician-patient transfusion risk assessment was developed using the best-performing clinically relevant XGBoost model. <strong>Results:</strong> A generalized linear model, random forest model, and XGBoost model were evaluated. The XGBoost model was trained with an existing dataset extracted from electronic medical records (n=1734). The area under the curve (AUC) was 0.71, precision-recall receiver operating characteristic curve (PR-ROC) was 0.82, and <i>F</i><sub>1</sub> score was 0.80. Performance on a physician-reviewed dataset (n=1734) was as follows: AUC=0.705, PR-ROC=0.78, and <i>F</i><sub>1</sub> score=0.809. Feature importance ranking and prediction were not clinically accurate for the pre-review dataset. <strong>Conclusions:</strong> Machine learning models are useful to determine an individual’s postpartum transfusion risk based on clinically variable and potentially modifiable factors, such as delivery mode, whether labor is induced, and delivery indications. In this study, the XGBoost model had a slightly higher AUC on structured data extracted from electronic medical records than the same dataset after physician review (AUC=0.71 and PR-ROC=0.82 vs AUC=0.705 and PR-ROC=0.78), but a slightly lower <i>F</i><sub>1</sub> score (<i>F</i><sub>1</sub>=0.80 vs <i>F</i><sub>1</sub>=0.809). XGBoost machine learning models trained on clinician-reviewed data can be used to predict postpartum transfusion. Clinically relevant, physician-labeled datasets are important for supervised machine learning model training for use in clinical decision support tools. Further study and external validation are needed prior to clinical use.
HHS responds coolly to paper on alcohol risk
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Automated Machine Learning Frameworks for Radiomics: Comparative Evaluation Study
<strong>Background:</strong> Automated machine learning (AutoML) frameworks can lower technical barriers for predictive and prognostic model development in radiomics by enabling researchers without programming expertise to build models. However, their effectiveness in addressing radiomics-specific challenges remains unclear. <strong>Objective:</strong> This study aimed to evaluate the performance, efficiency, and accessibility of general-purpose and radiomics-specific AutoML frameworks on diverse radiomics classification tasks, thereby guiding researchers and highlighting development needs for radiomics. <strong>Methods:</strong> A total of 10 public and private radiomics datasets with varied imaging modalities (computed tomography and magnetic resonance imaging), sizes, anatomies, and end points were used. Six general-purpose and 5 radiomics-specific frameworks were tested with predefined parameters using standardized cross-validation. Evaluation metrics included area under the receiver operating characteristic curve, runtime, and qualitative aspects related to software status, accessibility, and interpretability. <strong>Results:</strong> Simplatab, a radiomics-specific tool with a no-code interface, achieved the best overall balance between performance and computational efficiency, recording the highest average test area under the receiver operating characteristic curve (mean 78.46%, SD 12.22%) with a moderate runtime (1.1 h). However, its performance was not statistically superior to the most intensive general-purpose solutions. Most radiomics-specific frameworks were excluded from the performance analysis due to obsolescence, extensive programming requirements, or computational inefficiency. Conversely, general-purpose frameworks demonstrated higher accessibility and ease of implementation. <strong>Conclusions:</strong> While no single framework demonstrated absolute predictive superiority, Simplatab provides an effective balance of performance, efficiency, and accessibility for radiomics classification problems. However, continued efforts are needed to further mature AutoML solutions in the radiomics domain.
Educational Intervention on Environmentally Responsible Inhaler Prescribing Among French General Practitioners: Pilot Pre-Post Study
<strong>Background:</strong> Climate change is expected to cause more than 250,000 deaths annually by 2050 and could increase the prevalence of asthma and chronic obstructive pulmonary disease (COPD) by up to 30%. Pressurized metered-dose inhalers (pMDIs), primarily delivering short-acting beta-2 agonists, generate 15 to 30 times more greenhouse gas emissions than dry powder or soft mist inhalers. In France, short-acting beta-2 agonist pMDIs account for 95% of reliever therapy prescriptions, despite their limited effectiveness in controlling disease symptoms. <strong>Objective:</strong> This study aimed to evaluate the preliminary educational impact of a single educational session on French general practitioners’ awareness and intended prescribing of lower-carbon inhaler alternatives. <strong>Methods:</strong> We conducted a multicenter, single-group pre-post pilot study among 34 general practitioners from 10 multiprofessional health centers in Eastern Occitanie, France, between March and October 2023. Participants were recruited through convenience sampling. The intervention consisted of a one-time 25-minute face-to-face educational session on environmentally responsible inhaler prescribing, aligned with Global Initiative for Asthma (GINA) and Global Initiative for Chronic Obstructive Lung Disease guidelines. Data were collected using self-administered online questionnaires before the intervention and approximately 3 months later. The questionnaires included 2 clinical vignettes, one on asthma and one on COPD, with 3 prescribing questions each. Responses were categorized according to whether they included a pMDI. Changes in responses between baseline and follow-up were analyzed using the Fisher exact test or chi-square test, as appropriate. <strong>Results:</strong> A total of 34 participants completed the baseline questionnaire. Responses including a pMDI decreased from 70.6% (48/68) to 4% (3/68) for reliever therapy (<i>P</i><.001) and from 21.3% (29/136) to 4.4% (6/136) for maintenance therapy (<i>P</i>=.003). In asthma scenarios, adherence to GINA recommendations improved, with increased responses including inhaled corticosteroid-formoterol for reliever therapy (6%, 2/34 to 38%, 13/34; <i>P</i>=.001) and maintenance therapy (35%, 24/68 to 56%, 38/68; <i>P</i>=.02). No significant improvements were observed for COPD-related prescribing scenarios. The proportion of participants reporting environmental impact as a factor influencing inhaler choice increased from 3% (1/34) to 51% (18/34). Satisfaction was high, with 93% of participants reporting being very satisfied with the intervention. <strong>Conclusions:</strong> This pilot study suggests that a brief educational intervention may improve general practitioners’ knowledge and intended prescribing of lower-carbon inhaler alternatives, particularly in asthma scenarios. However, the outcomes were based on theoretical clinical vignettes rather than real-world prescribing data, and the study was not designed to assess the safety or clinical effectiveness of changing inhaler prescriptions. Future studies should evaluate sustained changes in real-world prescribing while ensuring individualized, clinically appropriate, and safe inhaler choices.
