People who are incarcerated face significantly higher health risks than the general population, yet deaths in custody remain underreported and poorly monitored by public health systems. Although the federal Death in Custody Reporting Act requires reporting of all deaths in correctional facilities to the US Department of Justice, reporting has been inconsistent, delayed, and often publicly inaccessible. Consequently, researchers have turned to press releases issued by correctional agencies as one of the few timely sources of information on deaths in custody. However, these press releases vary widely in content and structure, making standardized data extraction difficult. Crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) may offer a faster, low-cost method for gathering data, but their utility in this setting remains untested. This pilot study evaluated whether MTurk could be used to extract structured information from press releases about deaths in custody. We selected 144 press releases describing deaths between 2000 and 2023 from state prison systems and Immigration and Customs Enforcement. Each press release was assigned to 3 MTurk crowd workers (who were required to be English speaking and located in the United States), resulting in 432 individual responses. Workers were informed in advance that the task involved reviewing sensitive content related to deaths in custody. Crowd workers completed a 16-question form aligned with Death in Custody Reporting Act variables, including age, race and ethnicity, date of death, and facility location. Data quality was assessed using strict concordance (all 3 responses matched), 2-way concordance (2 of 3 responses matched), and qualitative review of common errors. Task completion time was also recorded. Sampling included complete subsets of selected press releases and a stratified subset from systems with more complex reporting formats. All 144 entries were completed within 48 hours. However, agreement across crowd workers was low: strict concordance was 14.2% (20/144) for age, 12.3% (18/144) for race or ethnicity, and 11.4% (16/144) for date of birth. Qualitative review identified frequent errors, missing data, and inattentive or automated responses. Crowd workers often misinterpreted system-specific terminology or copied placeholders instead of extracting information from the source. The low agreement indicated that this baseline MTurk configuration produced insufficient data quality for more resource-intensive use. MTurk enabled rapid task completion but produced low-quality results when applied to extracting structured data from carceral press releases. These findings suggest that general crowdsourcing platforms are poorly suited to complex data abstraction tasks without additional training or oversight. With improved task design or support from artificial intelligence tools, crowdsourcing may help address gaps in the surveillance of deaths in custody. Long-term improvements will require consistent, transparent, and standardized reporting practices across correctional institutions.

Emergency trauma care (ETC) in the "golden hour" can reduce the fatality rates by 20% - 50%. Imparting ETC training in undergraduate Indian medical graduate (IMG) curriculum can prepare them for both pre-hospital and hospital set-up management as a first responder, thus saving lives. However, teaching these lifesaving skills for managing acute trauma patient like spinal injury in competency based medical education (CBME) curriculum, is challenging. These skills can be taught on standardized patient, allowing the learner, to safely learn the skills on a living responding human being, thus achieving a higher level of clinical competency and better communication skills. However, their availability is limited. In such a scenario, video demonstration of such skills is an easy and reliable teaching learning method. The study aims to evaluate the effectiveness of using standardized patient versus video demonstration for teaching psycho-motor skills inthe management of spinal injury to under graduate students. A prospective interventional comparative study was carried out inthe Orthopaedics department of a teaching hospital from September 2024 to February 2025, with Institutional Ethics Committee approval. Thirty undergraduate students posted in Orthopaedics, were divided into two groups (15 each) based on their even and odd roll numbers (systematic sampling). One group was shown video demonstration (VD) ofskills like application of an improvised cervical collar and log rolling maneuver in the management of spinal injury patient in primary setting and other group was taught on a standardized patient (SP). The students were assessed by objective structured clinical examination (OSCE) and maneuvers were performed by them on mannequins. Pre-test and Post Test evaluation with peer validated questionnaire along with student's feedback was taken. The data was analyzed by unpaired t test for OSCE scores and paired t test for pre and post test scores with statistical significance set at p < 0.05 (Confidence interval 95%). Student feedback questionnaire based on 5-point Likert scale was evaluated. There was significant difference in the mean OSCE scores obtained by VD group 15.47 and by SP group is 18.27 (p value 0.004). However, in comparing the OSCE scores obtained in individual skill demonstration stations for log rolling maneuver and cervical spine immobilization (a pure psycho-motor skill), the mean scores of VD group were 2.27 and 3.20 and SP group was 2.33 and 3.27 respectively. This difference was not significant (p value 0.74 and 0.79 respectively). The student feedback showed teaching with standardized patient, was marginally more interesting and effective method, giving them more confidence to apply these skills in primary setting if needed. The study shows both methods are equally effective for teaching lifesaving psycho-motor skills in the management of spinal injury. However, SP group students had better student engagement . Hence a blended approach , where a video demonstration is used to teach large groups or can be given to students prior to the sessions, followed by teaching or assessing on standardized patient for application of these skills will lead to optimum results and save time.

Major depressive disorder (MDD) affects millions of Americans each year and is often diagnosed and treated in primary care. Evidence shows that self-management techniques, shared decision-making (SDM), and goal setting are effective strategies for managing MDD, but the required collaboration between patients and primary care clinicians can be difficult. Primary Care Path is a program for supporting depression management in primary care that includes a patient-facing mobile app and an accompanying care team-facing web interface. Leveraging programs that provide clinician-facing software with companion patient-facing mobile technology may help patients and physicians align depression treatment and management goals, support effective SDM, alleviate barriers, and improve both clinical care and patient outcomes. To pilot-test the use of Primary Care Path for MDD management in primary care and evaluate the impact of its use on depression treatment, symptoms, goal setting and attainment, and SDM. Four primary care clinical practices in the United States were assigned to program use (2 practices; intervention) versus usual care (2 practices; control). Intervention practices used the Primary Care Path program in their clinics and engaged patient participants in app use for 18 weeks. Clinical care teams engaged with the patient-informed program portal primarily during patient encounters (in-person, virtual or calls). Patient participants were smartphone users aged 18 years and older who were being treated for MDD. Patient participants received online surveys (medication changes, Patient Health Questionnaire-9 [PHQ-9], goal setting and attainment questions, and Shared Decision-Making Questionnaire-9 [SDM-Q-9]) at baseline, 6, 12, and 18 weeks. A total of 76 patient participants (34 intervention; 42 control) were enrolled; the majority were female (27/34, 79%; 32/42, 76%), White (31/34, 91%; 40/42, 95%), non-Hispanic/Latino/a (29/34, 85%; 40/40, 100%), and employed (26/34, 77%; 34/42, 81%). Control patient participants' conversations with their medical providers increased over the study period, while intervention patient conversations with their medical providers decreased over time. At week 18, intervention participants felt more successful than control in achieving their personalized treatment goals. More intervention patient participants initiated antidepressant medication by weeks 12 (P=.03) and 18 (P=.04) and switched medications by weeks 6 (P=.009) and 12 (P=.04) versus control. All patient participants demonstrated significant improvement in PHQ-9 scores throughout the study period (P<.001), with no difference in change by group. Clinicians and patients indicated using the program to support SDM, but no significant differences were observed in SDM-Q-9 between intervention and control. Preliminarily, the use of this digital health program related to earlier medication optimization, earlier conversations between patients and medical providers, and patient attainment of goals that matter most to them, indicating that coordinated use of the program by both patients and clinical team members may enhance MDD management in primary care clinical settings.

Bystander intervention is one of the most commonly used methods to curb the sexual violence crisis on college campuses. Most universities conduct training among their student bodies to ensure students are familiar with the procedure. However, it is necessary to remind and repeat messages to audiences to underscore their importance and solidify that knowledge among populations. In this study, the author developed Instagram-type reel messages that consider multiple frameworks used to develop bystander training and programs, such as the social norms approach and bystander barriers. These messages were tested via a 4×1 experimental design study with college students at a large public university (N=157). The conditions were messages that emphasize norms (norm reinforcement condition, based on the social norms approach; n=39), messages that emphasize behavior (norm readjustment condition, based on bystander barriers; n=39), messages that underscore the discrepancy between perceived peer norm and actual behavior (combination condition, based on both the social norms approach and bystander barriers; n=39), and a control condition (n=40). ANOVA analysis revealed that the norms reinforcement condition and the combination condition seemed to have the greatest impact on participants' perceived norms and behavioral intentions. In particular, when assessing norms as the dependent variable, the reinforcement group was significantly different from the readjustment group (Δmean 0.682, SD 0.160; P<.001), the combination group (Δmean 0.675, SD 0.160; P<.001), and the control group (Δmean 0.432, SD 0.159; P=.04). For behavioral intentions to engage in bystander intervention, the reinforcement group was significantly different from the control group (Δmean 0.217, SD 0.073; P=.02), and the combination group was significantly different from the control group as well (Δmean 0.221, SD 0.073; P=.02). The findings indicate that these messages could work in conjunction with training as a way to underscore the importance of bystander intervention behavior. It also highlights the role that Instagram reels can play in the prevention of sexual violence on college campuses.

In light of the growing use of artificial intelligence (AI) in health care, individuals' access to and use of health information are transforming. ChatGPT, an AI chatbot, provides immediate responses to health queries, with the potential to influence health-related attitudes, thereby raising concerns related to privacy, reliability, and security. This study aimed to investigate the perceived usefulness, risks, anxiety, and social influence of ChatGPT on health attitudes among users and nonusers in Saudi Arabia. A cross-sectional study was conducted using an online survey based on a validated tool. In total, 337 participants aged 18 years and older responded to questions assessing their perceptions of ChatGPT on health-related attitudes. Data showed that 76.1% (194/255) of the respondents used ChatGPT, with the majority being younger and more highly educated. The main uses for health-related purposes were health education (43/194, 22.2%) and physical activity guidance (31/194, 16%). The analysis showed that users considered ChatGPT useful for health-related decisions, with 45.9% (89/194) finding it easy to learn and use, but concerns about privacy (106/194, 54.7%) and reliability (87/194, 44.9%) remained. Among nonusers, security risks (39/61, 63.9%) were the major barrier to using AI-based tools for health purposes, and 68.9% (42/61) found such tools attractive and engaging. There were no statistically significant differences between users and nonusers across all examined sociodemographic characteristics (P>.05). The study established the potential of ChatGPT in improving health decision-making and revealed cultural, privacy, and trust issues that may affect its implementation. These findings underscore the importance of strengthening the security of AI-based applications to enhance public acceptability of related health policies and to support the safe integration of tools such as ChatGPT into the health care system.

Multimodal large language models (MLLMs) capable of integrating visual and textual information represent a promising advancement for clinical applications requiring image interpretation. Wound care assessment, which demands simultaneous analysis of wound photographs and clinical data, provides an ideal domain to evaluate multimodal vs unimodal artificial intelligence capabilities against human expertise. This study aims to compare the performance of MLLMs, unimodal ChatGPT 5.0, and human clinical experts on a standardized wound care certification examination. This cross-sectional comparative study evaluated 3 participant groups on a 25-question wound care certification examination spanning 4 clinical domains (Diagnosis, Treatment, Complication Management, and Wound Subtype Knowledge). Participants included 3 MLLMs (Med-PaLM 2, LLaVA-Med, and BioGPT), 1 unimodal large language model (ChatGPT 5.0), and 4 human clinical experts (general surgeon, wound care nurse, and 2 internal medicine physicians). Statistical analyses included one-way ANOVA with Tukey post hoc tests and domain-specific Kruskal-Wallis comparisons. Human experts achieved the highest accuracy (mean 86%, SD 9.1%), followed by MLLMs (mean 78.7%, SD 12.2%), while ChatGPT 5.0 achieved 64% accuracy, failing the 70% certification threshold. Significant overall group differences were observed (F2,5=8.42, P=.02, η²=0.74). MLLMs significantly outperformed ChatGPT 5.0 (difference=14.7 percentage points, P=.03, Cohen d=1.38), with the multimodal advantage most pronounced in visually dependent domains: Diagnosis (81% vs 43%, P=.008) and Complication Management (72% vs 50%, P=.03). No multimodal advantage was observed for text-based Wound Subtype Knowledge (both 67%). Med-PaLM 2 achieved 92% accuracy, matching that of the wound care nurse, while the general surgeon achieved the highest overall performance (96%). MLLMs demonstrate significant performance advantages over unimodal artificial intelligence in wound care assessment, particularly for visually dependent clinical tasks. While human experts with specialized wound care experience maintain overall superiority, the point estimate of the top-performing MLLM (Med-PaLM 2, 92%) fell within the observed range of human scores; however, the underpowered comparison (power=0.52) and wide CIs preclude definitive conclusions regarding noninferiority or equivalence to human experts. These findings support the potential role of MLLMs as clinical decision-support tools, warranting further adequately powered validation studies.

Approximately 1 in 3 pregnancies in the United States are complicated by one or more adverse pregnancy outcomes. This high prevalence contributes to the elevated rates of maternal and infant mortality in the United States. Modifiable prepregnancy or preconception lifestyle factors have been associated with adverse pregnancy outcomes in observational studies, which underscores the importance of preconception care. This cross-sectional descriptive study aimed to (1) estimate the prevalence of preconception lifestyle risk factors among women seeking services from a digital fertility platform, (2) characterize the study population and present relevant reference data, and (3) examine the distribution of prepregnancy lifestyle scores across demographic and clinical subgroups. The digital health company, Doveras Fertility, has built a prepregnancy digital health platform for individuals and couples seeking to optimize their fertility potential. Targeting users prior to initiation of pregnancy, the platform facilitates the assessment of baseline lifestyle risk factors. This paper reports on 396 adult women who sought the platform's services in a 1-month period between May and June 2024 by completing a digital fertility questionnaire. Self-reported data were analyzed for 6 healthy prepregnancy lifestyle factors known to be associated with maternal health outcomes in prior observational studies, and each participant was given a composite score between 0 to 6 to represent the number of these healthy behaviors reported. The 6 healthy prepregnancy lifestyle factors include a BMI of 18.5 to 24.9 kg/m2, not currently smoking, ≥150 min/week of moderate to vigorous physical activity, healthy eating, no daily alcohol intake, and use of a prenatal multivitamin. The study population was racially and ethnically diverse, with a mean age of 32.9 (SD 6.3) years. Most (235/396, 59%) participants received a composite score of 3 factors or fewer, and less than 5% (19/396) scored 6 out of 6. For context, this cohort had higher proportions of participants with unhealthy BMI and dietary patterns than those in the reference data. Regarding fertility, 46% (182/396) met the clinical definition of infertility (≥1 year trying to conceive), with the prevalence of infertility ranging from 16% (3/19) among those with the highest lifestyle scores to 59% (17/29) among those with the lowest. Most women seeking services from this digital fertility platform exhibited multiple lifestyle factors that have been previously associated with adverse pregnancy outcomes in observational studies. These results suggest that nearly all survey participants have potential risk factors for adverse maternal outcomes and therefore the potential to adopt at least one improvement in their lifestyle behavior. A digital platform may offer an accessible mechanism for identifying and characterizing preconception risk factors; however, future longitudinal studies are needed to evaluate whether platform-based interventions can effectively support behavior change and improve maternal health outcomes.

The aging and caregiving population is becoming increasingly diverse in the United States, leading to a growing need for culturally adapted interventions to address the unique needs of underrepresented groups, such as Asian Americans. However, interventions targeting Asian Americans and exploring cultural adaptation strategies remain limited in dementia caregiving research. This study aimed to describe the cultural adaptation process of an evidence-based intervention for Chinese and Korean American dementia caregivers, called the New York University Caregiver Intervention-Enhanced Support. We conducted a deductive content analysis and categorized our adaptation strategies into 5 elements: content, context, relationship fidelity and core elements, engagement, and cultural competence. Timing and types of responses to each adaptation strategy were also observed. Two authors conducted the initial analysis, and additional team members finalized the synthesis through discussion. The Template for Intervention Description and Replication (TIDieR) checklist was used to guide the methodological rigor. Twenty-four major adaptations were identified and categorized. For content, we translated materials, used culturally relevant terms, incorporated ethnic-specific surveys and resources, created social media support groups on platforms widely used by the targeted population, and extended the time allocated to complete the 6 counseling sessions. Context adaptation included expanding the range of individuals eligible for family counseling sessions to include fictive kin, using online and social media apps for communication, cultural matching and training of staff, and partnerships with relevant community organizations. Relationship fidelity and core elements involved consulting with community experts, conducting focus group interviews with caregivers, having regular meetings with the developer of the original intervention and an experienced New York University Caregiver Intervention-Enhanced Support clinician as well as experts in Chinese and Korean culture, and continuing regular counseling supervision. To enhance engagement, we provided clear explanations of the study procedure, which emphasized the benefits in participants' native languages and matched participants with social workers who shared the same cultural backgrounds. We also used a step-by-step contact approach and prolonged communication, explained staff roles to build rapport, and offered participant compensation. Finally, cultural competence was reflected in tailoring counseling techniques with respect for cultural beliefs, the use of euphemistic language for taboo subjects, and culturally appropriate refreshments to show respect and build interpersonal relationships. We systematically adjusted a counseling-based intervention, an approach less familiar among Asian Americans, to fit the cultural characteristics of the target population. A contribution of this study is using an integrated, theory-driven approach that combines 2 cultural adaptation frameworks while also capturing real-time adaptations informed by external feedback and self-reflection. This work provides a practical model for adapting evidence-based interventions to serve Chinese and Korean American dementia caregivers and may inform future adaptations for other East Asian populations.

Quality of life (QoL) is an important surgical outcome, commonly assessed through self-reports, and has the potential to be enhanced by objective information from personal technologies such as smartphone apps and wearables. Understanding patients' perspectives on this application of personal technologies is scarce. This study aimed to identify operational aspects of smartphone- and wearable-based assessments, as well as human and operational factors that may influence the acceptability of already owned (mostly smartphone) or new (mostly wearable) technologies by patients for pre- and post-surgery QoL assessments. Through purposive sampling, 41 patients from 3 health care centers in Switzerland, the United States, and the United Kingdom, who were undergoing or scheduled for surgery for degenerative cervical myelopathy (DCM), liver transplantation, or total hip replacement, were interviewed about their perceptions of QoL, current use of smartphones, health apps, and wearables for self-management and their views on using these technologies to assess QoL before and after surgery. Across the 3 studies (n=41), most (n=36) patients reported improved QoL after surgery, mainly due to reduced pain and fatigue and regained autonomy, while a few patients with DCM reported no change (n=2) or worsening (n=1). Patients were generally comfortable using smartphones and tablets, but few (n=4) used them for health management. Wearables were perceived differently across groups: they were well accepted in transplant@US, moderately in hip@UK, and least in myelopathy@CH. Many patients with DCM found wearables "useless," believing they added little to their self-awareness or recovery and could not replace human clinical judgment. Others expressed concerns about privacy, complexity, notifications, and battery life, while some acknowledged their motivational value when the data were clearly interpreted. Despite varying acceptance levels, most participants said they would consider using such devices if they contributed to research or improved care. Given a mostly negative attitude of patients toward wearables, we discuss the use of smartphone-based automated logging of physical functioning (sleep and physical activity) instead. Such logging may be less accurate than a dedicated wearable, but it may be accurate enough to measure their pre- and post-surgery physical functioning changes. Additionally, a smartphone has the advantage of being already well integrated into the daily life of patients from the perspective of its functionality and the patients' routines, contrary to wearable devices, which would have been provided to the patients in the context of pre- and post-surgery clinical care and require additional attention for their continuous wear, charging, and data synchronization, among others.

Youth experiencing early psychosis in West Africa often face delays in accessing evidence-based treatment. Digital mental health interventions may offer an acceptable and scalable approach to improve access to early psychosis care in West Africa; however, few data exist on the experiences and perspectives of patients with early psychosis and their caregivers to inform digital intervention development. This study aims to explore current experiences of early psychosis care, identify barriers and facilitators to in-person early psychosis care within health facilities, and identify opportunities for digital interventions to support patients with early psychosis and caregivers in Ghana. We conducted qualitative focus group discussions among patients with early psychosis, their caregivers, and their mental health care providers recruited at Accra Psychiatric Hospital in Accra, Ghana. Trained qualitative researchers facilitated discussions using a structured qualitative interview guide, exploring current care practices for early psychosis in Ghana, barriers and facilitators to facility-based care, and perceptions of digital mental health interventions. Transcripts were translated, transcribed, and analyzed thematically using a hybrid inductive and deductive approach grounded in the theoretical framework of acceptability. Overall, we conducted 4 focus group discussions (N=31) among 7 patients with early psychosis (median age 28, IQR 21-41 years), 6 caregivers (median age 58, IQR 29-34 years), and 18 clinicians (median age 30, IQR 29-34 years). Participants described current early psychosis care practices in Ghana, including seeking spiritual and traditional healing, the dearth of information and resources about psychosis, and the integral role of caregivers in facilitating treatment engagement and continuation (often at the cost of caregiver mental distress and burnout). Common barriers to facility-based mental health care included stigma associated with mental illness, lack of prior knowledge about early psychosis and treatment options, and practical constraints (eg, financial, logistical, and health care system limitations). Motivating factors for facility-based care included success stories from community members and strong rapport and trust in mental health clinicians. Technology (eg, mobile phones, laptops, radio, and television) was commonly used among participants in typical daily tasks, health information seeking, and stress reduction. Participants expressed support for digital tools that could deliver psychoeducation about early psychosis, support treatment adherence, and extend patient-provider communication between clinic visits. Digital mental health interventions have the potential to complement facility-based early psychosis services in Ghana by addressing misinformation, reducing access barriers, and supporting caregiver roles. These qualitative findings inform potential integration points, content, attributes, and strengths of digital modalities that could be leveraged to support patients with early psychosis and their caregivers in Ghana.