Organizational factors impacting the implementation of a digital mental health tool in Alberta's mental health care of youth and young adults by Marianne Barker, Julia Hews-Girard, Karina Pinston, Sarah Daniel, Lauren Volcko, Lia Norman, Emilie Bassi, Katherine Bright, Ian Hickie, Frank Iorfino, Haley LaMonica, Karen Moskovic, Melanie Fersovitch, Jessica Bradley, Leanne Stamp, Jason Gondziola, David Johnson, Gina Dimitropoulos

“…The study highlights a critical need for decision makers and clinical leaders to address organizational factors by integrating training and support, establishing interoperability between digitized and in-person healthcare systems, and leveraging support for MBC and youth-centred care.…”
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Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea by Hyunji Sang, Jaeyu Park, Soeun Kim, Myeongcheol Lee, Hojae Lee, Sun-Ho Lee, Dong Keon Yon, Sang Youl Rhee

“…Fifteen variables related to patient profiles were integrated and applied to various ML algorithms, including random forest, support vector, XGBoost, decision tree, and AdaBoost regressors. …”
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Built environment change for injury prevention: insights from public health professionals across public health units in Ontario, Canada by Emily McCullogh, Alison Macpherson, Daniel W. Harrington, Ian Pike, Brent Hagel, Claire Buchan, Pamela Fuselli, Sarah A. Richmond

“…Their perspectives offer valuable insights that can support future BE change work in the Ontario public health context, thus illustrating the importance of including practitioners’ voices in injury prevention research. …”
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Long-term mindfulness meditation increases occurrence of sensory and attention brain states by Daniel Yochai Panitz, Daniel Yochai Panitz, Daniel Yochai Panitz, Avi Mendelsohn, Avi Mendelsohn, Avi Mendelsohn, Joana Cabral, Joana Cabral, Joana Cabral, Aviva Berkovich-Ohana, Aviva Berkovich-Ohana

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Diagnosis of Protected Agriculture in Imbabura—Ecuador, Period 2016–2023 by Luis Marcelo Albuja-Illescas, Andrés Manolo Carrión-Burgos, Rafael Jiménez-Lao, María Teresa Lao

“…The findings could inform the formulation of public policies or specific interventions to strengthen protected agriculture in the region; however, support mechanisms are needed to fully exploit its potential. …”
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The Influence of Parents on Adolescent Independence: A Study in the Context of Islamic Boarding Schools and Its Implications for Counseling by Hanan Ainul Birri Anwar Munawar, Arif Rahman Hakim, Ananda Saadatul Maulidia

“…Additionally, tailored counseling interventions that focus on building resilience, self-efficacy, and decision-making skills are essential for addressing the unique challenges faced by students in Islamic boarding schools. …”
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Technical and management coaching for government institutions: Lessons learned and health systems transformations across 8 countries in sub-Saharan Africa and India. by Kate Graham, Anna Kalbarczyk, Lekan Ajijola, Emily Das, Fatimata Sow, Kenneth Owino, Maheen Malik

“…This strengthened local governments' capacity to lead and sustain the scale-up of FP and AYSRH programs beyond TCI's direct support. Improved monitoring and tracking of coaching quality is needed for further insights.…”
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Continuum of Care - Building bridges between different phases of the patient's pathway by Michael Adolph, Marek Lichota

“…Ahead of surgical procedures, the patient´s condition is improved in the best possible way by means of a multimodal prehabilitation approach combining different interventions, e.g., exercise, patient education, psychologic support, and nutrition. …”
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ColombiaTuitionSET: Labeled dataset for exploring socioeconomic status, career selection, and tuition fees at a Colombian public universityMendeley Data by Fernan A. Villa-Garzon, Maria A. Muñoz-Alarcon, John W. Branch-Bedoya

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Impact of an adaptive environment based on learning analytics on pre-service science teacher behavior and self-regulation by Yousri Attia Mohamed Abouelenein, Shaimaa Abdul Salam Selim, Tahani Ibrahim Aldosemani

“…Abstract Learning analytics provides valuable data to inform the best decisions for each learner. This study, based on adaptive environment (AE) learning analytics dashboards, examines how instructor interventions affect student self-regulation abilities and academic performance. …”
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Determinants of Health Seeking Behavior among Caregivers of Infants Admitted with Acute Childhood Illnesses at Kenyatta National Hospital, Nairobi, Kenya by Winfred Muringi Wambui, Samuel Kimani, Eunice Odhiambo

“…Provision of a seamless supply system, infrastructural support, and technical support for soft skills minimize the turnaround time which is critical.…”
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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

“…A comparative analysis of linear regression, K-nearest neighbors regression, decision tree regression, random forest regression, XGBoost regression, lasso regression, and support vector regression is conducted to predict affinity energy. …”
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Predictive factors for the development of depression in children and adolescents: a clinical study by Hong Zhang, Peilin Yu, Xiaoming Liu, Xiaoming Liu, Ke Wang, Ke Wang, Ke Wang, Ke Wang

“…Also, the DCA and calibration curves demonstrate excellent performance.ConclusionValidated nomogram can accurately predict the risk of depression in children and adolescents, providing clues for clinical practitioners to develop targeted interventions and support.…”
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Understanding Electric Bikers’ Red-Light Running Behavior: Predictive Utility of Theory of Planned Behavior vs Prototype Willingness Model by Tianpei Tang, Hua Wang, Xizhao Zhou, Hao Gong

“…Our findings also support designing of more effective behavior-change interventions to better target e-bikers’ RLR behavior by considering the influence of the identified psychological factors.…”
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Exploring the quality of end-of-life care in the intensive care unit: a qualitative multiple case study approach with family members of Muslim patients by Robert Fowler, Craig Dale, Ayah Nayfeh, Lesley Gotlib Conn

“…Four central themes were identified: (1) trust and confidence in the healthcare team overseen by medical experts; (2) quality communication with medical experts; (3) achieving patient goals of care; and (4) dignity of care through cultural respect and emotional support. Culture, religion and religiosity did not appear to have a major influence on the medical decision-making process. …”
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Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms by Jinmei Liu, Juan Luo, Xu Chen, Jiyi Xie, Cong Wang, Hanxiang Wang, Qi Yuan, Shijun Li, Yu Zhang, Jianli Hu, Chen Shi

“…The DCA showed that clinical interventions for patients at high risk of opioid nonadherence based on the LR model can benefit patients. …”
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Community engagement initiatives in primary health care to achieve universal health coverage: A realist synthesis of scoping review. by Daniel Erku, Resham Khatri, Aklilu Endalamaw, Eskinder Wolka, Frehiwot Nigatu, Anteneh Zewdie, Yibeltal Assefa

“…<h4>Results</h4>Themes related to the structural aspects of CE initiatives included the methodological approaches (i.e., format and composition), levels of CE (i.e., extent, time, and timing of engagement) and the support processes and strategies (i.e., skills and capacity) that are put in place to enable both communities and service providers to undertake successful CE. …”
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An interpretable machine learning tool for in-home monitoring of agitation episodes in people living with dementia: a proof-of-concept studyResearch in context by Marirena Bafaloukou, Ann-Kathrin Schalkamp, Nan Fletcher-Lloyd, Alex Capstick, Chloe Walsh, Cynthia Sandor, Samaneh Kouchaki, Ramin Nilforooshan, Payam Barnaghi

“…We incorporated a traffic-light system to stratify agitation probability estimates supporting clinical decision-making, and employed the SHapley Additive exPlanations (SHAP) framework to enhance interpretability. …”
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Understanding ICU Nursing Knowledge, Perceived Barriers, and Facilitators of Sepsis Recognition and Management: A Cross-Sectional Study by Katherine A. Kissel, MN, RN, Karla D. Krewulak, PhD, Thérèse G. Poulin, BSc, Ken Kuljit S. Parhar, MD, MSc, Daniel J. Niven, MD, PhD, Vanessa M. Doiron, MScHQ, RN, Kirsten M. Fiest, PhD

“…Fewer nurses agreed it was easy to identify priority sepsis interventions based on order urgency (n = 53, 53%, TDF domain Memory, Attention, and Decision Processes). …”
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Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients by Hongsheng Wu, Biling Liao, Tengfei Ji, Keqiang Ma, Yumei Luo, Shengmin Zhang

“…Among various machine learning models, which included Bagged Tree, Boost Tree, Decision Tree, LightGBM, Naïve Bayes, Nearest Neighbors, Support Vector Machine (SVM), and Random Forest (RF), the one with the maximum area under the curve (AUC) was chosen for model construction. …”
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