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Efficacy of a Web-Based Integrated Growth Mindset Intervention on Reducing Anxiety Among Social Work and Counseling Practicum Trainees: Protocol for a 2-Arm Randomized Controlled Trial

Efficacy of a Web-Based Integrated Growth Mindset Intervention on Reducing Anxiety Among Social Work and Counseling Practicum Trainees: Protocol for a 2-Arm Randomized Controlled Trial

Practicum is an essential educational component of professional training [1,2], bridging the gap between theory and practice and enhancing the professional capacity and core competence of future practitioners [3,4]. The quality of fieldwork profoundly influences social work trainees’ personal, intellectual, and professional development [4], per communication skills, critical reflection, professional growth, creativity, innovation, and self-efficacy [1,5].

Yongyi Wang, An Xi, Stella S K Wong, Kong Yam, Janet Tsin Yee Leung, Shimin Zhu

JMIR Res Protoc 2025;14:e67234

Perceived Trust and Professional Identity Threat in AI-Based Clinical Decision Support Systems: Scenario-Based Experimental Study on AI Process Design Features

Perceived Trust and Professional Identity Threat in AI-Based Clinical Decision Support Systems: Scenario-Based Experimental Study on AI Process Design Features

According to an analysis of Germany-wide Diagnosis Related Groups statistics, there was an incidence of 158 patients with sepsis per 100,000 inhabitants in Germany in 2015. The proportion of sepsis patients among all hospital patients was 0.7%. 53.8% of patients with hospital-acquired sepsis were treated in intensive care units and 41.7% died in hospital [56].

Sophia Ackerhans, Kai Wehkamp, Rainer Petzina, Daniel Dumitrescu, Carsten Schultz

JMIR Form Res 2025;9:e64266

Codevelopment of an mHealth App With Health Care Providers, Digital Health Experts, Community Partners, and Families for Childhood Obesity Management: Protocol for a Co-Design Process

Codevelopment of an mHealth App With Health Care Providers, Digital Health Experts, Community Partners, and Families for Childhood Obesity Management: Protocol for a Co-Design Process

An evidence-based, theory-driven mobile health (m Health) app that targets parents is a critical missing link in currently available interventions to support parental self-management of childhood obesity [3]. While there has been a rapid growth of m Health apps to support chronic disease management, achieving meaningful health improvements through the use of m Health apps remains elusive.

Siao Hui Toh, Courtney Davis, Khairunisa Bte Khaider, Zhi Quan Ong, Ethel Jie Kai Lim, Chu Shan Elaine Chew

JMIR Res Protoc 2025;14:e59238

Evaluation of the Efficacy of the Traditional Chinese Medicine Formulation Ru-Yi-Jin-Huang-Saan on Colles Fracture After Surgery: Protocol for a Randomized, Double-Blind, Placebo-Controlled Trial

Evaluation of the Efficacy of the Traditional Chinese Medicine Formulation Ru-Yi-Jin-Huang-Saan on Colles Fracture After Surgery: Protocol for a Randomized, Double-Blind, Placebo-Controlled Trial

Therefore, we have designed an experiment to verify the curative effect of the external application of TCM in fracture surgery. Ru-Yi-Jin-Huang-Saan (RYJHS) is a TCM herbal patch composed of a fixed blend of TCM ingredients combined with water. It is traditionally applied to relieve swelling and pain in the early stages of musculoskeletal injuries, attributed in TCM theory to its heat-clearing and swelling-reducing properties. 

Lien-Cheng Lin, Wei-Hsun Wang, Wei-Kai Chang, Jyun-Liang Gao, Ru-Chang Yang, Po-Chi Hsu, Lun-Chien Lo

JMIR Res Protoc 2025;14:e56849

Correlation Between Diagnosis-Related Group Weights and Nursing Time in the Cardiology Department: Cross-Sectional Study

Correlation Between Diagnosis-Related Group Weights and Nursing Time in the Cardiology Department: Cross-Sectional Study

In the meantime, studies in China have also shown that DRG systems can be used as an evaluation index for the quality of medical services, work efficiency, operating costs, and performance [11].

Chen Lv, Yi-Hong Gong, Xiu-Hua Wang, Jun An, Qian Wang, Jing Han, Xiao-Feng Chen

JMIR Med Inform 2025;13:e65549

Deep Learning–Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study

Deep Learning–Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study

Electrocardiogram (ECG) is an old tool in clinical medicine but has re-emerged for the prediction of low left ventricular ejection fraction [17], arrhythmia [18], dyskalemia [19], or even longer-term mortality [20].

Shao-Chi Lu, Guang-Yuan Chen, An-Sheng Liu, Jen-Tang Sun, Jun-Wan Gao, Chien-Hua Huang, Chu-Lin Tsai, Li-Chen Fu

J Med Internet Res 2025;27:e67576

Predicting Agitation-Sedation Levels in Intensive Care Unit Patients: Development of an Ensemble Model

Predicting Agitation-Sedation Levels in Intensive Care Unit Patients: Development of an Ensemble Model

The study’s aim was to develop an ensemble learning model for the continuous evaluation of agitation and sedation levels in patients admitted to ICUs. The model is expected to facilitate patient monitoring, provide early warnings about patient behavior, increase assessment frequency, and enable automatic evaluation of patient conditions with treatment suggestions.

Pei-Yu Dai, Pei-Yi Lin, Ruey-Kai Sheu, Shu-Fang Liu, Yu-Cheng Wu, Chieh-Liang Wu, Wei-Lin Chen, Chien-Chung Huang, Guan-Yin Lin, Lun-Chi Chen

JMIR Med Inform 2025;13:e63601

Comparative Effectiveness of Wearable Devices and Built-In Step Counters in Reducing Metabolic Syndrome Risk in South Korea: Population-Based Cohort Study

Comparative Effectiveness of Wearable Devices and Built-In Step Counters in Reducing Metabolic Syndrome Risk in South Korea: Population-Based Cohort Study

If any of these 5 health behaviors were not practiced before the program but were adopted afterward, it was considered an improvement in health behaviors.

Kyung-In Joung, Sook Hee An, Joon Seok Bang, Kwang Joon Kim

JMIR Mhealth Uhealth 2025;13:e64527

Leveraging Large Language Models for Infectious Disease Surveillance—Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis

Leveraging Large Language Models for Infectious Disease Surveillance—Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis

Reinfection often refers to the phenomenon in which an individual who has recovered from COVID-19 is again infected with the virus [31]. Some researchers [32,33] considered that reinfection is identified when an individual tests positive again through polymerase chain reaction (PCR) testing after a minimum of 90 days of a negative result. However, some studies also suggest this duration should be 30 days [34,35].

Jiacheng Xie, Ziyang Zhang, Shuai Zeng, Joel Hilliard, Guanghui An, Xiaoting Tang, Lei Jiang, Yang Yu, Xiufeng Wan, Dong Xu

J Med Internet Res 2025;27:e63190

Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study

Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study

Precision-recall curves have been considered an effective metric for accessing the model, especially the data is an unbalanced dataset [15,16]. The optimal probability threshold is where a point can achieve high precision score while only sacrificing minimal recall (Figure S1 in Multimedia Appendix 1). By calculating the accurate prediction rate of the AOP model and the rule-based method (right-left and left-right), we can compare the performance between these 2 methods.

Yuan-Hsin Chen, Ching-Hsuan Lin, Chiao-Hsin Fan, An Jim Long, Jeremiah Scholl, Yen-Pin Kao, Usman Iqbal, Yu-Chuan Jack Li

JMIR Form Res 2025;9:e68436