Pakistan Journal of Allied Health Sciences (PJAHS)
Scope and Objectives Pakistan Journal of Allied Health Sciences (PJAHS) is a peer-reviewed journal published quarterly in the months of March, June, September and December. The journal is open to researches carried out in medical, allied and health sciences. Types of manuscripts accepted in this journal included original research papers, short communications, review articles, case reports, editorials, book reviews, biographical notes and conference reports. Objectives: To provide a platform to researchers for dissemination high quality research conducted in the field of health sciences To provide readers information about new researches conducted in medical, allied and health sciences To be a valid source of scientific communication To encourage good publication practices
Smart Hospital Technologies and Operational Efficiency: The Mediating Role of System Integration
The healthcare industry is experiencing rapid digital transformation through the adoption of smart hospital technologies that enhance healthcare delivery, resource management, and organizational performance. Smart hospital technologies encompass advanced digital solutions such as the Internet of Things (IoT), artificial intelligence, cloud computing, robotics, electronic health records, real time monitoring systems, and automated decision support tools. These technologies enable healthcare organizations to improve clinical processes, optimize resource utilization, and enhance operational outcomes. Among the most important organizational outcomes is operational efficiency, which reflects the ability of hospitals to utilize resources effectively while maintaining high quality healthcare services. Although smart hospital technologies are expected to improve operational efficiency, the effectiveness of these technologies may depend on the degree of system integration within healthcare institutions. System integration refers to the seamless connection and coordination of various technological systems, applications, databases, and healthcare processes. Integrated systems facilitate information sharing, communication, interoperability, and coordinated decision making across departments. Therefore, this study investigates the relationship between smart hospital technologies and operational efficiency while examining the mediating role of system integration. Drawing upon the Technology Organization Environment Framework and Information Systems Success Theory, the study proposes that smart hospital technologies positively influence operational efficiency through enhanced system integration. A quantitative research design was employed using survey data collected from healthcare administrators, information technology professionals, healthcare managers, and clinical personnel working in hospitals. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS SEM) through SmartPLS software. The findings reveal that smart hospital technologies significantly improve system integration and operational efficiency. Furthermore, system integration significantly mediates the relationship between smart hospital technologies and operational efficiency. The study contributes to healthcare management, health informatics, and digital transformation literature by explaining the mechanisms through which technological innovation enhances hospital performance. Practical implications are provided for healthcare leaders, policymakers, and technology developers seeking to improve operational excellence in healthcare organizations.
Smart Hospital Technologies, Operational Efficiency, System Integration, Healthcare Informatics, Digital Transformation, SmartPLS, Hospital Management, Mediation Analysis
Copyright (c) 2026 Pakistan Journal of Allied Health Sciences (PJAHS)
Hospital Data Systems and Infection Control: The Mediating Role of Surveillance Accuracy
The increasing integration of digital technologies into healthcare has transformed the management of clinical information and public health monitoring. Hospital data systems have become essential tools for collecting, storing, processing, and analyzing healthcare information to support clinical decision making and operational efficiency. One of the most critical applications of hospital data systems is infection control, which aims to prevent, detect, and manage healthcare associated infections that threaten patient safety and healthcare quality. Effective infection control requires timely identification of infection patterns, monitoring of outbreaks, and implementation of preventive interventions. Hospital data systems facilitate these functions by providing accurate and accessible information for healthcare professionals. However, the effectiveness of hospital data systems in improving infection control may depend on the accuracy of surveillance processes. Surveillance accuracy refers to the precision, completeness, timeliness, and reliability of infection monitoring activities conducted within healthcare organizations. Accurate surveillance enables early detection of infections, effective response strategies, and evidence based decision making. Therefore, this study investigates the relationship between hospital data systems and infection control while examining the mediating role of surveillance accuracy. Drawing upon Information Systems Success Theory and Epidemiological Surveillance Theory, the study proposes that hospital data systems positively influence infection control through enhanced surveillance accuracy. A quantitative research design was employed using survey data collected from healthcare professionals working in hospitals and healthcare institutions. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS SEM) through SmartPLS software. The findings reveal that hospital data systems significantly improve surveillance accuracy and infection control performance. Furthermore, surveillance accuracy significantly mediates the relationship between hospital data systems and infection control. The study contributes to healthcare informatics, infection prevention, and hospital management literature by explaining the mechanisms through which digital information systems improve infection control outcomes. Practical implications are provided for healthcare administrators, policymakers, and technology developers seeking to strengthen infection prevention strategies.
Hospital Data Systems, Infection Control, Surveillance Accuracy, Healthcare Informatics, SmartPLS, Healthcare Associated Infections, Mediation Analysis, Digital Health Systems
Precision Medicine and Treatment Effectiveness: The Moderating Role of Genetic Variability
Precision medicine has emerged as a transformative approach in modern healthcare by tailoring medical treatments to individual patient characteristics, including genetic, environmental, and lifestyle factors. Unlike traditional healthcare models that apply standardized treatments to broad patient populations, precision medicine seeks to optimize therapeutic outcomes by considering biological differences among individuals. Advances in genomics, bioinformatics, molecular diagnostics, and artificial intelligence have accelerated the implementation of precision medicine across various medical fields, including oncology, cardiology, neurology, and rare disease management. One of the primary objectives of precision medicine is to improve treatment effectiveness by delivering targeted therapies that are more likely to produce favorable outcomes while minimizing adverse effects. However, the effectiveness of precision medicine may vary among patients due to differences in genetic variability. Genetic variability refers to the natural differences in genetic makeup among individuals, including gene mutations, polymorphisms, and genomic variations that influence biological responses to medical interventions. Genetic variability may affect drug metabolism, therapeutic responsiveness, disease susceptibility, and treatment outcomes. Therefore, this study investigates the relationship between precision medicine and treatment effectiveness while examining the moderating role of genetic variability. Drawing upon Pharmacogenomics Theory and Personalized Medicine Theory, the study proposes that precision medicine positively influences treatment effectiveness and that genetic variability significantly moderates this relationship. A quantitative research design was employed using survey data collected from healthcare professionals, clinical researchers, and patients receiving precision medicine based interventions. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS SEM) through SmartPLS software. The findings reveal that precision medicine significantly improves treatment effectiveness. Furthermore, genetic variability significantly moderates the relationship between precision medicine and treatment effectiveness. The study contributes to precision healthcare, genomics, and personalized medicine literature by identifying the importance of genetic factors in determining therapeutic outcomes. Practical implications are provided for healthcare providers, researchers, and policymakers seeking to enhance the effectiveness of personalized treatment strategies.
Precision Medicine, Treatment Effectiveness, Genetic Variability, Pharmacogenomics, Personalized Medicine, SmartPLS, Healthcare Innovation, Moderation Analysis
Digital Public Health Systems and Disease Prevention: The Mediating Role of Awareness Dissemination
The rapid evolution of digital technologies has transformed public health systems by enabling more efficient disease surveillance, communication, and prevention strategies. Digital public health systems integrate electronic health records, mobile health applications, social media platforms, and data analytics to enhance health service delivery and population health management. Despite their increasing adoption, limited empirical research has examined the mechanisms through which digital public health systems contribute to disease prevention outcomes. This study investigates the mediating role of awareness dissemination in the relationship between digital public health systems and disease prevention. Drawing upon Health Communication Theory, Diffusion of Innovations Theory, and Preventive Health Behavior Theory, the study proposes that digital public health systems improve disease prevention by facilitating timely and widespread dissemination of health awareness information. A quantitative cross sectional research design is proposed using data collected from healthcare professionals, public health officials, and community health workers. Data are analyzed using Partial Least Squares Structural Equation Modeling (PLS SEM) via SmartPLS 4. The findings indicate that digital public health systems significantly influence both awareness dissemination and disease prevention. Awareness dissemination partially mediates the relationship between digital public health systems and disease prevention. The results highlight that digital systems are most effective when they enhance public awareness, health literacy, and behavioral responsiveness. This study contributes to public health informatics, health communication, and preventive medicine by explaining the cognitive and informational mechanisms underlying digital health interventions. The findings offer practical implications for policymakers, healthcare organizations, and technology developers aiming to strengthen disease prevention strategies through improved communication systems.
Digital Public Health Systems, Disease Prevention, Awareness Dissemination, Health Communication, SmartPLS, Mediation Analysis, Public Health Informatics, Preventive Health Behavior, Digital Health, Health Literacy
AI in Radiology and Diagnostic Accuracy: The Moderating Role of Radiologist Experience and Patient Load
Artificial Intelligence (AI) has emerged as one of the most transformative technologies in modern healthcare, particularly in the field of radiology where image interpretation plays a critical role in disease diagnosis and treatment planning. AI powered radiological systems utilize machine learning, deep learning, and computer vision algorithms to analyze medical images, identify abnormalities, and support clinical decision making. These technologies have demonstrated significant potential in improving diagnostic accuracy, reducing interpretation errors, and enhancing healthcare efficiency. Diagnostic accuracy is essential for ensuring appropriate treatment decisions, minimizing medical errors, and improving patient outcomes. While AI technologies are increasingly integrated into radiological practice, their effectiveness may depend on contextual and human factors. Radiologist experience reflects the knowledge, expertise, and clinical competence accumulated through education and professional practice, whereas patient load refers to the volume of patients and diagnostic cases managed within a specific period. Experienced radiologists may be better equipped to interpret and validate AI generated recommendations, thereby maximizing diagnostic performance. Conversely, high patient load may increase time pressure and cognitive fatigue, potentially influencing the effectiveness of AI supported diagnostic processes. Therefore, this study investigates the relationship between AI in radiology and diagnostic accuracy while examining the moderating roles of radiologist experience and patient load. Drawing upon Sociotechnical Systems Theory and Human Capital Theory, the study proposes that AI in radiology positively influences diagnostic accuracy and that radiologist experience and patient load significantly moderate this relationship. A quantitative research design was employed using survey data collected from radiologists, radiology technicians, healthcare administrators, and medical imaging professionals. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS SEM) through SmartPLS software. The findings reveal that AI in radiology significantly improves diagnostic accuracy. Furthermore, radiologist experience strengthens, while patient load weakens, the relationship between AI in radiology and diagnostic accuracy. The study contributes to radiology, healthcare technology, and medical informatics literature by identifying key factors influencing AI effectiveness in clinical practice.
Artificial Intelligence, Radiology, Diagnostic Accuracy, Radiologist Experience, Patient Load, SmartPLS, Medical Imaging, Healthcare Technology
Mobile Health Applications and Chronic Disease Management: The Mediating Role of Self Care Behavior
The rapid expansion of mobile technologies has significantly transformed healthcare delivery, particularly in the management of chronic diseases. Mobile health applications, commonly known as mHealth apps, provide patients with tools for monitoring health conditions, tracking symptoms, receiving medication reminders, and accessing real time health information. These applications have become essential in supporting long term disease management for conditions such as diabetes, hypertension, cardiovascular diseases, and asthma. Despite their growing popularity, the effectiveness of mobile health applications in improving chronic disease management outcomes depends on behavioral mechanisms such as self care behavior. This study examines the relationship between mobile health applications and chronic disease management while investigating the mediating role of self care behavior. Chronic disease management refers to the coordinated healthcare processes aimed at improving the quality of life, reducing complications, and enhancing health outcomes for individuals living with long term health conditions. Effective management requires continuous monitoring, medication adherence, lifestyle modification, and regular communication with healthcare providers. Mobile health applications support these processes by providing digital platforms for patient engagement, health tracking, and personalized feedback. However, the success of these technologies largely depends on patients’ willingness and ability to engage in self care practices. Self care behavior refers to individuals’ proactive actions to maintain health, manage symptoms, and prevent complications associated with chronic diseases. These behaviors include medication adherence, dietary regulation, physical activity, symptom monitoring, and timely healthcare consultation. Mobile health applications may enhance self care behavior by increasing awareness, motivation, and self monitoring capabilities. This study employs a quantitative research design using survey data collected from patients diagnosed with chronic diseases who use mobile health applications. Structural Equation Modeling using SmartPLS is applied to examine the direct relationship between mobile health application usage and chronic disease management outcomes as well as the mediating effect of self care behavior. The findings indicate that mobile health applications significantly improve chronic disease management. Furthermore, self care behavior acts as a significant mediator, strengthening the relationship between mHealth usage and health outcomes. The study contributes to digital health, nursing, public health, and healthcare management literature by explaining how mobile technologies influence chronic disease outcomes through behavioral mechanisms. The findings provide practical implications for healthcare providers, app developers, and policymakers aiming to improve patient engagement and long term health outcomes. Future research should explore additional mediators such as health literacy, motivation, and digital adherence.
Mobile Health Applications, Chronic Disease Management, Self-Care Behavior, M Health, Digital Health, Patient Engagement, Smart PLS, Healthcare Technology
Tele Pharmacy Services and Medication Adherence: The Mediating Role of Patient Education
The expansion of digital health technologies has reshaped pharmaceutical care delivery, with tele pharmacy services emerging as a key innovation in improving access to medication counseling, prescription management, and patient follow up. Tele pharmacy enables pharmacists to provide remote services such as medication review, drug interaction checks, adherence counseling, and chronic disease support through digital communication platforms. Despite its growing adoption, the effectiveness of tele pharmacy in improving medication adherence is not solely dependent on technological availability but also on behavioral and educational mechanisms. This study examines the relationship between tele pharmacy services and medication adherence while investigating the mediating role of patient education. Medication adherence refers to the extent to which patients take medications as prescribed in terms of dosage, timing, and frequency. Poor adherence is a major global healthcare challenge, contributing to treatment failure, disease complications, and increased healthcare costs. Tele pharmacy services are designed to address these issues by improving access to pharmacists and providing continuous medication support. However, the success of these services depends significantly on how well patients understand their treatment regimens. Patient education refers to the structured process of providing individuals with knowledge, skills, and understanding about their medications, disease conditions, and treatment plans. Effective patient education improves awareness, enhances self management, and strengthens adherence behavior. Tele pharmacy services can enhance patient education through remote counseling, digital reminders, and interactive communication tools. This study employs a quantitative research design using survey data collected from patients receiving tele pharmacy services. Structural Equation Modeling using SmartPLS is applied to examine the direct relationship between tele pharmacy services and medication adherence as well as the mediating role of patient education. The findings indicate that tele pharmacy services significantly improve medication adherence. Furthermore, patient education plays a significant mediating role in this relationship. The study contributes to pharmacy practice, digital health, and healthcare management literature by explaining how tele pharmacy improves adherence through educational mechanisms. The findings provide practical implications for pharmacists, healthcare providers, and policymakers. Future research should explore additional mediators such as trust in pharmacists, digital literacy, and healthcare accessibility.
Tele pharmacy, Medication Adherence, Patient Education, Digital Pharmacy, Pharmaceutical Care, SmartPLS, Healthcare Communication, Digital Health
Robotic Surgery and Outcomes: The Moderating Role of Surgeon Skill Level and Clinical Experience
Robotic surgery has emerged as a major advancement in modern healthcare, enabling minimally invasive procedures with enhanced precision, improved visualization, and reduced patient recovery time. Despite its technological sophistication, the clinical outcomes of robotic surgery vary significantly across hospitals and surgeons. This study examines the relationship between robotic surgery adoption and surgical outcomes, with a focus on the moderating roles of surgeon skill level and clinical experience. Drawing on Socio Technical Systems Theory, Skill Acquisition Theory, and Learning Curve Theory, the study proposes that robotic surgery improves surgical outcomes, while surgeon skill level and clinical experience strengthen this relationship by enhancing precision, decision making, and procedural efficiency. A quantitative cross sectional research design is proposed involving surgeons, surgical residents, operating room staff, and hospital administrators. Data are analyzed using Partial Least Squares Structural Equation Modeling (PLS SEM) via SmartPLS 4. The findings indicate that robotic surgery significantly improves surgical outcomes, while both surgeon skill level and clinical experience significantly moderate this relationship. The results suggest that highly skilled and experienced surgeons achieve superior outcomes when using robotic surgical systems compared to less experienced operators. This study contributes to surgical informatics and healthcare technology literature by identifying human performance boundaries in robotic assisted surgery. The findings offer practical implications for hospital administrators, surgical training programs, and policymakers aiming to optimize robotic surgery outcomes.
Robotic Surgery, Surgical Outcomes, Surgeon Skill Level, Clinical Experience, SmartPLS, Moderation Analysis, Surgical Informatics, Minimally Invasive Surgery, Learning Curve, Healthcare Technology
Nursing Digital Tools and Care Quality: The Moderating Role of Workload Pressure and Leadership Support
Nursing digital tools have become increasingly important in modern healthcare systems for improving documentation accuracy, patient monitoring, communication, and clinical decision support. These tools include electronic health records, mobile nursing applications, digital medication systems, and patient monitoring dashboards. While nursing digital tools are widely expected to enhance care quality, their effectiveness is not uniform across clinical settings. This study examines the relationship between nursing digital tools and care quality, with a focus on the moderating roles of workload pressure and leadership support. Drawing on Socio Technical Systems Theory, Job Demands Resources Theory, and Transformational Leadership Theory, the study proposes that nursing digital tools improve care quality, while workload pressure and leadership support influence the strength of this relationship. A quantitative cross sectional research design is proposed involving nurses, nurse managers, and clinical supervisors. Data are analyzed using Partial Least Squares Structural Equation Modeling (PLS SEM) via SmartPLS 4. The findings indicate that nursing digital tools significantly improve care quality. Workload pressure negatively moderates this relationship, while leadership support positively moderates it. The results suggest that high workload reduces the effectiveness of digital tools, whereas strong leadership enhances their impact. This study contributes to nursing informatics and healthcare management literature by identifying key organizational conditions affecting digital tool effectiveness in nursing practice. The findings provide practical implications for hospital administrators and nursing leaders aiming to improve care quality through digital transformation.
Nursing Digital Tools, Care Quality, Workload Pressure, Leadership Support, Nursing Informatics, SmartPLS, Moderation Analysis, Job Demands Resources Theory, Healthcare Management, Digital Nursing Systems
Clinical Decision Support Systems and Patient Safety: The Mediating Role of Error Reduction
Clinical Decision Support Systems (CDSS) have become a central component of modern healthcare digital transformation, assisting clinicians in making accurate, timely, and evidence based decisions. These systems integrate patient data, clinical guidelines, and predictive analytics to support diagnosis, treatment selection, and medication management. Despite widespread adoption, the impact of CDSS on patient safety varies across healthcare settings. This study examines the relationship between Clinical Decision Support Systems and patient safety, with a focus on the mediating role of error reduction. Drawing on Information Processing Theory, Sociotechnical Systems Theory, and the Swiss Cheese Model of patient safety, the study proposes that CDSS improves patient safety by reducing clinical, diagnostic, and medication errors. A quantitative cross sectional research design is proposed involving physicians, nurses, pharmacists, and healthcare IT professionals. Data are analyzed using Partial Least Squares Structural Equation Modeling (PLS SEM) via SmartPLS 4. The findings indicate that CDSS significantly improves patient safety, while error reduction significantly mediates this relationship. The results suggest that CDSS enhances safety outcomes primarily by minimizing human and system related errors. This study contributes to health informatics and patient safety literature by clarifying the mechanism through which CDSS improves healthcare outcomes. The findings provide practical implications for hospital administrators, policymakers, and system designers aiming to optimize CDSS implementation for safer healthcare delivery.
Clinical Decision Support Systems, Patient Safety, Error Reduction, Health Informatics, SmartPLS, Mediation Analysis, Information Processing Theory, Swiss Cheese Model, Healthcare Quality, Digital Health Systems
Community Health Interventions and Disease Prevention: The Moderating Role of Health Awareness and Behavioral Change
Community health interventions have become a central strategy for improving public health outcomes and preventing the spread of communicable and non-communicable diseases. Governments and health organizations increasingly rely on community-based programs to promote disease prevention, improve health behaviors and strengthen public awareness about health risks. Despite the implementation of various intervention programs, the effectiveness of these initiatives often depends on individual awareness levels and the willingness of communities to adopt positive health behaviors. This study examines the impact of community health interventions on disease prevention while exploring the moderating roles of health awareness and behavioral change. The research is grounded in health promotion theory and behavioral change models which emphasize the importance of awareness and behavioral transformation in improving public health outcomes. Community health interventions such as vaccination campaigns, health education programs, sanitation initiatives and preventive screenings aim to reduce disease prevalence by encouraging healthier lifestyles and improving access to preventive services. However, the success of these interventions varies significantly depending on how well individuals understand health risks and how effectively they modify their behaviors. A quantitative research design was used to analyze the relationships among the study variables. Data were collected from individuals participating in community health programs using a structured questionnaire. Structural equation modeling with Smart PLS was employed to examine the direct relationship between community health interventions and disease prevention as well as the moderating effects of health awareness and behavioral change. The results indicate that community health interventions significantly improve disease prevention outcomes. Furthermore, health awareness and behavioral change strengthen the effectiveness of these interventions by encouraging individuals to adopt healthier lifestyles and preventive practices. Communities with higher awareness and stronger behavioral adaptation demonstrate better health outcomes and reduced disease risk. This study contributes to public health research by providing empirical evidence regarding the mechanisms through which community health programs influence disease prevention. The findings highlight the importance of integrating health education and behavior change strategies into community interventions in order to maximize their impact. Policymakers and healthcare organizations can use these insights to design more effective public health programs that promote sustainable health improvements and reduce the burden of disease.
Community Health Interventions, Disease Prevention, Health Awareness, Behavioral Change, Public Health Promotion, Preventive Healthcare
Copyright (c) 2025 Pakistan Journal of Allied Health Sciences (PJAHS)
Workplace Stress and Healthcare Professional Burnout: The Moderating Role of Organizational Support and Emotional Resilience
Workplace stress has become one of the most critical challenges faced by healthcare professionals worldwide. The demanding nature of healthcare environments combined with high patient expectations, long working hours, and emotional exposure to suffering often leads to severe psychological strain among medical staff. Persistent exposure to occupational stress may result in professional burnout which is characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment. Burnout among healthcare professionals not only affects individual wellbeing but also reduces quality of care, patient safety, and overall healthcare system performance. Therefore, understanding the factors that influence the relationship between workplace stress and professional burnout has become an important area of research. The present study investigates the relationship between workplace stress and healthcare professional burnout while examining the moderating roles of organizational support and emotional resilience. Organizational support refers to the degree to which healthcare institutions provide resources, recognition, and psychological support to their employees. Emotional resilience represents the ability of healthcare professionals to cope with stress and adapt effectively to challenging workplace situations. These factors may reduce the negative impact of workplace stress on burnout by providing psychological and institutional resources that enhance coping capabilities. This study adopts a quantitative research approach using a structured questionnaire distributed among healthcare professionals including doctors, nurses, and allied health staff. Data were collected from 285 respondents working in public and private healthcare institutions. Smart PLS structural equation modeling was used to analyze the proposed research model and test the moderating relationships between workplace stress, organizational support, emotional resilience, and burnout. The results indicate that workplace stress has a significant positive impact on healthcare professional burnout. However organizational support and emotional resilience significantly moderate this relationship by weakening the negative impact of workplace stress. These findings highlight the importance of supportive organizational environments and psychological resilience in protecting healthcare professionals from burnout. The study provides valuable insights for healthcare administrators and policymakers to develop strategies that improve employee wellbeing and sustain healthcare workforce performance.
Workplace Stress, Healthcare Burnout, Organizational Support, Emotional Resilience, Healthcare Workforce
Digital Mental Health Interventions and Psychological Wellbeing: The Mediating Role of Patient Engagement and Self-Management Behaviors
Digital mental health interventions have emerged as a transformative approach to addressing global mental health challenges. With the increasing prevalence of anxiety, depression, and stress related disorders, digital platforms such as mobile health applications, telepsychology services, and online cognitive behavioral therapy programs have become significant tools in providing accessible psychological care. The purpose of this study is to investigate the impact of digital mental health interventions on psychological wellbeing while examining the mediating roles of patient engagement and self-management behaviors. Psychological wellbeing is considered a multidimensional construct that includes emotional stability, life satisfaction, and resilience. Digital interventions provide continuous monitoring, therapeutic content, and behavioral guidance that encourage individuals to actively participate in their own mental health management. However, the effectiveness of these interventions largely depends on the level of engagement and the development of positive self-management behaviors among users. This research adopts a quantitative research design using survey data collected from individuals who actively use digital mental health platforms. The proposed conceptual framework integrates elements from the Technology Acceptance Model and Self Determination Theory to explain how digital interventions influence behavioral engagement and self-management practices which subsequently enhance psychological wellbeing. Data analysis is conducted using Smart PLS structural equation modeling to test the relationships among variables and evaluate mediation effects. The findings demonstrate that digital mental health interventions significantly improve psychological wellbeing. Additionally patient engagement and self-management behaviors play crucial mediating roles that strengthen the positive impact of digital interventions. The results highlight the importance of designing user centered digital mental health platforms that foster active participation and encourage self-regulated coping strategies. The study contributes to the growing body of literature on digital health and psychological wellness by providing empirical evidence on the behavioral mechanisms that drive the success of digital mental health programs. Practical implications suggest that healthcare providers, policymakers, and technology developers should focus on enhancing engagement features and promoting self-management strategies to maximize the benefits of digital mental health interventions.
Digital Mental Health Interventions, Psychological Well-Being, Patient Engagement, Self-Management Behaviors, Digital Health Technologies
Antimicrobial Stewardship Programs and Antibiotic Resistance Control: The Mediating Role of Clinical Compliance and Institutional Policy
Antimicrobial resistance (AMR) has emerged as a major global health threat, resulting in increased morbidity, mortality, and healthcare costs. Inappropriate and excessive use of antibiotics in healthcare settings is a key driver of antibiotic resistance, highlighting the urgent need for effective control strategies. Antimicrobial stewardship programs (ASPs) are evidence-based interventions designed to optimize the use of antimicrobials, improve patient outcomes, and reduce the development of antibiotic resistance. These programs include clinical guidelines, prescription audits, feedback mechanisms, and educational initiatives for healthcare professionals. Despite their potential, the effectiveness of ASPs often depends on mediating factors such as clinical compliance and institutional policy support. Clinical compliance refers to the adherence of healthcare professionals to established stewardship guidelines, while institutional policies represent the structural and regulatory support provided by healthcare organizations to facilitate appropriate antibiotic use. This study investigates the impact of antimicrobial stewardship programs on antibiotic resistance control while examining the mediating roles of clinical compliance and institutional policy. A quantitative research design was adopted, with data collected through structured questionnaires administered to healthcare professionals, including physicians, pharmacists, and infection control staff in hospitals. Structural equation modeling using Smart PLS was employed to analyze the relationships between ASP implementation, clinical compliance, institutional policy, and outcomes in antibiotic resistance control. The results demonstrate that antimicrobial stewardship programs significantly reduce antibiotic resistance. Clinical compliance and institutional policy partially mediate this relationship, indicating that the effectiveness of ASPs is enhanced when healthcare professionals adhere to guidelines and institutions provide robust policy support. The findings underscore the importance of combining behavioral, organizational, and structural interventions to maximize the success of ASPs. This research contributes to the growing literature on antimicrobial stewardship and provides practical insights for healthcare administrators and policymakers to implement effective programs for antibiotic resistance control.
Antimicrobial Stewardship Programs, Antibiotic Resistance Control, Clinical Compliance, Institutional Policy, Healthcare Governance
Health System Resilience and Pandemic Preparedness: The Mediating Role of Crisis Management Capacity and Public Health Governance
Health systems across the world have faced unprecedented pressure during recent global health emergencies, particularly during the COVID 19 pandemic. The crisis revealed significant weaknesses in preparedness, coordination and response mechanisms within many national health systems. Health system resilience has therefore emerged as a critical concept in global public health discourse, emphasizing the ability of healthcare systems to anticipate, absorb, adapt and recover from public health emergencies. This study examines the relationship between health system resilience and pandemic preparedness while exploring the mediating role of crisis management capacity and public health governance. The research aims to analyze how effective governance structures and crisis response capabilities influence the overall readiness of healthcare systems to handle large scale public health emergencies. The study adopts a quantitative research design using structural equation modeling through Smart PLS to examine the relationships among key variables. Data were collected from healthcare administrators, public health professionals and policymakers working in hospitals and public health institutions. The conceptual framework proposes that health system resilience significantly influences pandemic preparedness both directly and indirectly through crisis management capacity and public health governance. Crisis management capacity reflects the ability of healthcare institutions to respond quickly and effectively to health emergencies, while public health governance refers to institutional coordination, policy effectiveness and regulatory support in managing public health threats. The findings reveal that health system resilience has a strong positive relationship with pandemic preparedness. Furthermore, crisis management capacity and public health governance significantly mediate this relationship, indicating that resilient systems perform better when supported by strong governance frameworks and well-developed crisis response capabilities. The study contributes to the growing literature on health system strengthening and emergency preparedness by providing empirical evidence regarding the structural mechanisms that improve pandemic response outcomes. The findings highlight the importance of investing in governance reforms, institutional coordination and crisis management training to build resilient healthcare systems capable of addressing future global health emergencies.
Health System Resilience, Pandemic Preparedness, Crisis Management Capacity, Public Health Governance, Healthcare Systems, Global Health Security
The Role of Physiotherapy in Long-COVID Functional Rehabilitation
Long-COVID, characterized by persistent symptoms following acute SARS-CoV-2 infection, presents a significant challenge to global healthcare systems. Patients report ongoing fatigue, dyspnea, musculoskeletal weakness, cognitive dysfunction, and reduced exercise tolerance, all of which impair daily functioning and quality of life (Carfì et al., 2020). Functional rehabilitation aims to restore mobility, strength, cardiovascular fitness, and independence in activities of daily living. Physiotherapy, as a cornerstone of rehabilitation, integrates exercise prescription, respiratory interventions, neuromuscular facilitation, and patient education to optimize recovery outcomes. This study examines the role of physiotherapy in long-COVID functional rehabilitation and evaluates its impact on functional status, fatigue, respiratory capacity, and quality of life. A conceptual model was developed to hypothesize that physiotherapy interventions positively influence functional outcomes through mediators including adherence, symptom management, and patient engagement. Using a quantitative cross-sectional survey design, data were collected from 200 post-COVID patients attending outpatient rehabilitation centers. Standardized instruments measured physiotherapy exposure, functional independence (using Barthel Index), fatigue (using Fatigue Severity Scale), respiratory function (spirometry), and quality of life (SF-36). Structural equation modeling (SEM) using SmartPLS evaluated direct and indirect relationships. Results indicate that physiotherapy significantly improves functional independence (β = 0.53, p < .001), reduces fatigue (β = –0.42, p < .001), enhances respiratory function (β = 0.47, p < .001), and improves overall quality of life (β = 0.49, p < .001). Adherence and patient engagement mediated these effects, suggesting that active participation is critical for optimal outcomes. The measurement model demonstrated strong reliability (Cronbach’s alpha > 0.8) and validity (AVE > 0.5). Structural paths confirmed hypothesized relationships, explaining 61 percent of variance in functional outcomes. These findings highlight physiotherapy as a pivotal component of long-COVID rehabilitation, emphasizing individualized exercise programs, respiratory techniques, and patient education to restore function and independence. Challenges include variable symptom severity, patient adherence, and resource constraints. Recommendations include tele-rehabilitation programs, integration of multidisciplinary teams, standardized protocols for long-COVID physiotherapy, and patient-centered approaches to optimize adherence. This study contributes empirical evidence supporting physiotherapy as a core intervention to mitigate long-term functional impairments in post-COVID populations, informing clinical practice and policy development.
Long-Covid, Physiotherapy, Functional Rehabilitation, Fatigue, Respiratory Function, Patient Engagement, Structural Equation Modeling
The Role of Nursing Informatics in Patient Safety Enhancement
This research examines the pivotal role of Nursing Informatics (NI) in enhancing patient safety within healthcare settings. Nursing Informatics integrates nursing science with information management and analytical sciences to optimize health outcomes (McGonigle & Mastrian, 2021). The accelerating complexity of patient care, combined with mounting safety concerns, has made NI an essential contributor to reducing clinical errors, improving data accuracy, and fostering evidence-based decision-making. This study explores how NI tools—such as Electronic Health Records (EHRs), Clinical Decision Support Systems (CDSS), and real-time reporting technologies—shape patient safety practices. The literature indicates that NI enables timely access to accurate patient information, supports standardized care protocols, and facilitates predictive analytics for risk mitigation (Sensmeier, 2019; Staggers et al., 2018). Using SmartPLS structural equation modeling, this study tests a conceptual model linking NI adoption, data quality, clinician decision support, and patient safety outcomes. A quantitative survey design was employed, capturing data from registered nurses across acute care facilities. Analysis demonstrated that NI adoption significantly predicts improved data quality (β = 0.62, p < 0.001) and enhanced decision support (β = 0.47, p < 0.001), both of which significantly contribute to patient safety enhancement (β = 0.59 and 0.51 respectively, p < 0.001). Findings affirm that NI is not only a technological enabler but also a safety catalyst when integrated effectively into nursing workflows. Challenges remain, including training gaps, workflow disruption, and interoperability issues. Recommendations include expanding NI education, aligning clinical workflows with informatics tools, and investing in interoperable systems. This research contributes to nursing leadership and healthcare informatics literature by demonstrating that meaningful engagement with NI correlates with measurable improvements in patient safety.
Nursing Informatics, Patient Safety, Electronic Health Records, Clinical Decision Support Systems, Healthcare Quality,
The Health Misinformation on Social Media and Preventive Health Behaviors
Health misinformation on social media has emerged as a significant public health challenge affecting individuals’ engagement with preventive health behaviors. Social media platforms such as Facebook, Twitter, Instagram, and WhatsApp play central roles in disseminating health content, yet they also enable rapid spread of inaccurate, misleading, or unverified information (World Health Organization, 2020). Preventive health behaviors refer to actions individuals take to prevent disease onset or progression, including vaccination, healthy lifestyle practices, screenings, and adherence to public health recommendations. Misinformation can distort perceptions of risk, undermine trust in science and health authorities, and reduce compliance with evidence-based preventive measures. Understanding the relationship between exposure to health misinformation on social media and preventive health behaviors is crucial for designing interventions that enhance public health outcomes. This study examines how health misinformation exposure influences preventive health behaviors and whether mediators such as trust in health authorities and health literacy can mitigate this impact. A conceptual model was developed hypothesizing that higher exposure to health misinformation is negatively associated with preventive health behaviors, while trust and health literacy buffer this relationship. Using a quantitative cross-sectional survey design, data were collected from adults who regularly use social media for health information. Measures included self-reported exposure to misinformation, frequency of preventive health behaviors, trust in health authorities, and health literacy levels. Structural equation modeling was conducted using SmartPLS to test hypothesized paths. Results indicate that exposure to health misinformation significantly predicts lower engagement in preventive health behaviors (β = –0.48, p < .001). Trust in health authorities positively predicts preventive behaviors (β = 0.36, p < .001) and moderates the misinformation effect, reducing its negative influence. Health literacy also shows a positive association with preventive behaviors (β = 0.29, p < .01) and weakens the misinformation impact. The model explains a substantial proportion of variance in preventive behaviors, suggesting these mediators are important protective factors. These findings highlight that misinformation on social media poses real risks to public health by discouraging evidence-based preventive actions. Interventions that strengthen trust in health institutions and enhance health literacy may reduce susceptibility to misinformation and promote better health outcomes. Public health campaigns, digital literacy programs, and platform-level policies targeting misinformation are recommended to foster an informed public capable of engaging in preventive health behaviors.
Health Misinformation, Social Media, Preventive Health Behaviors, Health Literacy, Trust In Health Authorities, Structural Equation Modeling
Remote Patient Monitoring and Chronic Disease Control Outcomes
Chronic diseases such as diabetes, hypertension, cardiovascular disease, and chronic respiratory conditions are leading causes of morbidity and mortality worldwide, posing substantial economic and healthcare burdens (World Health Organization, 2021). Effective management of chronic conditions relies heavily on consistent monitoring, timely interventions, and patient engagement. Remote Patient Monitoring (RPM) leverages digital health technologies to collect and transmit patient data, enabling healthcare providers to monitor vital signs, medication adherence, and clinical parameters in real time (Steinberg et al., 2020). RPM offers opportunities to improve chronic disease outcomes, enhance patient engagement, reduce hospitalizations, and optimize resource utilization. This study examines the impact of RPM on chronic disease control outcomes, including glycemic levels, blood pressure, cardiovascular markers, and hospitalization rates. A conceptual model was developed positing that RPM influences clinical outcomes both directly and indirectly via patient engagement, adherence to treatment protocols, and self-management behaviors. A quantitative cross-sectional survey was conducted among 250 patients with chronic diseases enrolled in RPM programs across multiple outpatient clinics. Standardized instruments measured RPM usage, patient engagement, adherence, and disease-specific clinical outcomes. Structural equation modeling (SEM) using SmartPLS evaluated hypothesized relationships. Results reveal that RPM significantly predicts improved clinical outcomes, including better glycemic control (β = –0.41, p < .001), reduced blood pressure variability (β = –0.37, p < .001), and decreased hospitalization rates (β = –0.34, p < .001). Patient engagement (β = 0.36, p < .001) and treatment adherence (β = 0.32, p < .01) mediated these relationships, indicating that active involvement and compliance with monitoring protocols enhance effectiveness. The measurement model demonstrated high reliability (Cronbach’s alpha > 0.8) and validity (AVE > 0.5). Structural paths explained 63 percent of variance in chronic disease outcomes. These findings suggest that RPM is an effective intervention for chronic disease management, particularly when combined with strategies to promote engagement and adherence. Implementation challenges include technology access, patient digital literacy, data privacy concerns, and integration with healthcare systems. Future directions include longitudinal studies assessing long-term impact, personalized RPM algorithms, and integration of AI-driven analytics to enhance predictive care. RPM represents a scalable, patient-centered approach that can improve chronic disease control and reduce the burden on healthcare systems.
Remote Patient Monitoring, Chronic Disease Management, Patient Engagement, Treatment Adherence, Clinical Outcomes, Structural Equation Modeling
Patient-Centered Care Models and Treatment Adherence in Chronic Diseases
This study examines how patient-centered care models influence treatment adherence among individuals with chronic diseases. Chronic diseases such as diabetes, cardiovascular disease, chronic respiratory conditions, and hypertension present persistent treatment challenges that require long-term patient engagement with care plans. Treatment adherence, defined as the extent to which a patient follows prescribed medical regimens, medication schedules, lifestyle adjustments, and therapeutic recommendations, profoundly affects health outcomes, quality of life, and healthcare costs (World Health Organization, 2003). Patient-centered care emphasizes tailoring healthcare delivery to individual patient needs, values, preferences, and active engagement in decision making (Institute of Medicine, 2001). Core elements of patient-centered care include shared decision making, comprehensive communication, emotional support, care coordination, and respect for patient preferences. These elements may foster greater trust, understanding, and empowerment, thereby promoting adherence. Despite theoretical support, empirical evidence about the mechanisms linking patient-centered practices with adherence in chronic illness remains fragmented. Using a quantitative cross-sectional design, this study collected survey data from patients diagnosed with one or more chronic conditions across multiple outpatient clinics. A validated questionnaire measured perceptions of patient-centered care dimensions and self-reported adherence. Structural equation modeling (SEM) via SmartPLS tested relationships between care model constructs and adherence outcomes. Findings reveal that shared decision making (β = 0.34, p < .001), communication quality (β = 0.29, p < .001), and care coordination (β = 0.27, p < .01) each significantly predict higher treatment adherence. Emotional support showed a smaller but still significant effect (β = 0.18, p < .05). The model explained 56 percent of variance in adherence behaviors, suggesting that patient-centered care processes play a meaningful role in supporting chronic disease management. These results reinforce the importance of clinical practices that go beyond biomedical interventions to foster relational, cognitive, and structural support for patients. Implications include training clinicians in effective communication, redesigning care pathways for greater shared decision making, and implementing systems that support coordinated care. Challenges include measuring adherence objectively and ensuring representativeness across diverse populations. Future research should explore longitudinal effects and intervention studies that build patient agency and integrate behavioral support mechanisms. Investing in patient-centered systems not only aligns with ethical care principles but also offers practical pathways to improved adherence and better chronic disease outcomes
Patient-Centered Care, Chronic Disease, Treatment Adherence, Shared Decision Making, Communication Quality, Care Coordination
The Impact of Ethical Legal and Social Implications on Digital Health Technologies
Digital health technologies have transformed modern healthcare delivery by enabling remote monitoring data driven decision making and patient centered care. Innovations such as electronic health records telemedicine mobile health applications and artificial intelligence based clinical systems promise improved efficiency accessibility and quality of care. However, alongside these benefits ethical legal and social implications have emerged as critical determinants shaping the adoption acceptance and sustainability of digital health technologies. This research examines how ethical legal and social considerations influence trust acceptance and effective use of digital health technologies within healthcare systems. The study is grounded in socio technical systems theory and technology acceptance perspectives which emphasize that technological outcomes are shaped by social values institutional frameworks and regulatory environments. Ethical considerations such as data privacy informed consent algorithmic transparency and fairness directly affect patient trust and willingness to engage with digital platforms. Legal implications including data protection laws liability frameworks and regulatory compliance shape organizational readiness and innovation capacity. Social implications such as digital literacy cultural acceptance and equity influence how different population groups experience digital health solutions. A quantitative research design was employed using survey data collected from healthcare professionals’ patients and health administrators. Partial least squares structural equation modeling was applied to test the relationships among ethical legal and social implications digital health adoption and perceived effectiveness. Findings indicate that ethical transparency and legal compliance significantly enhance trust and acceptance of digital health technologies while social inclusion and digital literacy moderate these relationships. Ethical considerations emerged as the strongest predictor of trust followed by legal safeguards and social equity. The study contributes to digital health scholarship by empirically validating the central role of ethical legal and social factors in shaping technology outcomes. Practical implications highlight the need for integrated governance frameworks that balance innovation with responsibility. Policymakers’ technology developers and healthcare leaders must address ethical legal and social dimensions proactively to ensure sustainable and equitable digital health transformation.
Digital Health Ethics Legal Implications Social Implications Technology Acceptance Trust
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Impact of AI-Assisted Radiography on Diagnostic Accuracy and Workload
The integration of artificial intelligence (AI) into diagnostic radiography has rapidly transformed clinical imaging practices. AI-assisted radiography refers to the use of machine learning algorithms and deep learning models to support image interpretation, detect abnormalities, and prioritize cases. Proponents argue that AI can enhance diagnostic accuracy while alleviating clinician workload, yet empirical evidence remains mixed, and the mechanisms linking AI use to clinical outcomes are not fully understood. This study investigates the impact of AI-assisted radiography systems on diagnostic accuracy and radiology workload, examining radiologist trust, workflow integration, and perceived ease of use as mediating and moderating factors. A cross-sectional survey was conducted among 720 radiologists and radiologic technologists across tertiary care hospitals adopting AI tools. Five latent constructs were measured: AI Integration Strength, Perceived Ease of Use, Radiologist Trust in AI, Diagnostic Accuracy, and Workload Impact. Data were analyzed using statistical tools structural equation modeling to assess direct, indirect, and interaction effects. Results indicate that stronger AI integration positively predicts perceived ease of use (β = .457, p < .001) and radiologist trust in AI (β = .399, p < .001). Perceived ease of use significantly influences diagnostic accuracy (β = .378, p < .001) and reduces workload impact (β = −.342, p < .001). Radiologist trust moderates the relationship between ease of use and diagnostic accuracy (β = .185, p = .002), suggesting that trust enhances the positive effect of AI tools on diagnostic outcomes. Mediation tests reveal that perceived ease of use partially mediates the relationship between AI integration and both diagnostic accuracy and workload. These findings demonstrate that successful implementation of AI in radiography is not solely a technological endeavor but is shaped by user perceptions and trust. Hospitals should focus on training, usability optimization, and trust-building strategies to maximize AI’s clinical benefits. Enhancing diagnostic accuracy while reducing radiologist workload can improve patient outcomes, throughput, and staff satisfaction. The results provide actionable insights for policymakers, clinical leaders, and informatics teams designing AI deployment strategies in imaging departments
Assistive Technologies and Quality of Life in Physically Disabled Patients
Assistive technologies play a critical role in improving the functional independence and overall quality of life of physically disabled patients. As global disability prevalence rises due to aging populations chronic diseases and trauma related injuries the demand for effective assistive solutions has increased substantially. This study examines the impact of assistive technologies on quality of life by integrating physical functional outcomes psychological wellbeing and social participation into a unified explanatory model. Grounded in health technology acceptance and social determinants of health perspectives the study proposes that accessibility usability and perceived effectiveness of assistive technologies significantly enhance quality of life outcomes. Furthermore, patient empowerment and healthcare support are examined as mediating mechanisms that strengthen this relationship. A quantitative research design was employed using survey data collected from physically disabled patients receiving care in rehabilitation centers and teaching hospitals. Statistical tools were applied to test the measurement and structural models using partial least squares structural equation modeling. Results demonstrate that assistive technology use has a statistically significant positive effect on quality of life. Accessibility and usability emerged as the strongest predictors while healthcare support partially mediated the relationship between technology use and patient wellbeing. The model explains a substantial proportion of variance in quality of life indicating strong explanatory power. The findings contribute to existing literature by offering empirical evidence that assistive technologies influence not only physical functioning but also psychological autonomy and social inclusion. Practical implications highlight the importance of user centered design training programs and institutional support to maximize benefits of assistive technologies. Policymakers’ healthcare providers and rehabilitation professionals should prioritize equitable access and sustained support mechanisms to reduce disability related inequalities. This study concludes that assistive technologies are not merely functional tools but integral components of holistic patient centered care. Future research should adopt longitudinal designs and explore emerging digital and AI enabled assistive systems to further understand long term quality of life outcomes
Assistive Technologies Quality Of Life Physical Disability Rehabilitation Healthcare Support
Antibiotic Stewardship Programs and Antimicrobial Resistance Patterns in Teaching Hospitals
Antimicrobial resistance (AMR) is a growing global health threat, undermining the effectiveness of antibiotics and jeopardizing clinical outcomes, particularly in hospital settings. Teaching hospitals serve as critical sites for both high-intensity antibiotic use and training of healthcare professionals, making them vital arenas for implementation of Antibiotic Stewardship Programs (ASPs). ASPs are structured interventions designed to optimize antibiotic usage, reduce inappropriate prescribing, and curb the evolution of resistant pathogens. This study investigates the relationships between the implementation strength of ASPs and antimicrobial resistance patterns observed in teaching hospitals, incorporating mediating effects of prescriber behavior and moderating effects of institutional support.A cross-sectional survey of 650 healthcare professionals and retrospective review of antibiogram data from three urban teaching hospitals were conducted. Five latent constructs were measured: ASP Implementation Strength, Prescriber Behavior, Institutional Support, Antibiotic Utilization Patterns, and AMR Patterns. Structural equation modeling assessed direct, indirect, and interaction effects among constructs to reveal pathways by which stewardship interventions influence resistance outcomes. Results showed that stronger ASP implementation significantly improved prescriber behavior (β = .422, p < .001) and optimized antibiotic utilization (β = .387, p < .001), which in turn were associated with lower prevalence of resistant organisms (β = −.321, p < .001). Institutional support moderated the ASP → Prescriber Behavior link (β = .179, p = .002), indicating that hospitals with greater resources and leadership engagement achieved greater behavior change. Mediation tests confirmed that prescriber behavior and utilization patterns partially mediated the relationship between ASP implementation and observed AMR patterns. These findings highlight the multifactorial impact of stewardship interventions on resistance dynamics in teaching hospitals. They underscore that beyond policy existence, prescriber practices and institutional context critically shape outcomes. Strengthening stewardship frameworks, enhancing education, and securing administrative support are essential for sustainable reductions in AMR. Results inform hospital policy, training curricula, and resource allocation strategies to improve antibiotic use and mitigate resistance trends.
Antibiotic Stewardship, Antimicrobial Resistance, Teaching Hospitals, Structural Equation Modeling,
Social Determinants of Health and Inequities in Maternal Healthcare Access
Maternal healthcare access remains uneven across populations globally. Persistent inequities rooted in social determinants of health such as socioeconomic status, education, geographic location, and cultural norms result in disproportionate maternal morbidity and mortality. This thesis examines how core social determinants influence maternal healthcare access and outcomes, proposing a conceptual model that identifies pathways through which structural and individual factors generate inequities. To investigate relationships among socioeconomic status, educational attainment, geographic location, health system responsiveness, and maternal healthcare utilization; and to quantify these relationships using structural equation modeling with SmartPLS. A cross-sectional survey of 600 women of reproductive age (18–49 years) was conducted across urban and rural districts. Latent constructs measured included Socioeconomic Status, Health System Responsiveness, Education Level, Geographic Accessibility, and Maternal Healthcare Utilization. Data were analyzed using SmartPLS 4 to assess measurement reliability and structural relationships, including direct, indirect, and moderated effects. Measurement model assessments demonstrated strong reliability (CR > .80) and validity (AVE > .50) for all constructs. Structural paths were statistically significant (p < .001) demonstrating that socioeconomic status and education positively predicted utilization of maternal healthcare services, while geographic barriers negatively affected access. Health system responsiveness mediated the link between social determinants and utilization. Interaction terms suggested that educational level moderated the effect of health system responsiveness on maternal care uptake. Social determinants significantly influence maternal healthcare access. Evidence supports that improvements in socioeconomic conditions and educational attainment can increase utilization, particularly when health systems are responsive and accessible. Geographic inequities remain a barrier, especially in rural settings. Policy interventions must address structural inequities and strengthen health services responsiveness to achieve equitable maternal healthcare access
Maternal Healthcare Access, Social Determinants Of Health, Inequities, Smartpls, Structural Equation Modeling.
Digital Documentation Systems and Clinical Workflow Efficiency: Evidence from Healthcare Professionals
Digital documentation systems (DDS), particularly electronic health records and integrated clinical documentation platforms, have become central to healthcare digital transformation. This study investigates the impact of digital documentation systems on clinical workflow efficiency, examining the mediating role of user satisfaction and the moderating influence of organizational IT support. Grounded in the Technology Acceptance Model and the DeLone and McLean Information Systems Success Model, a quantitative cross-sectional survey was conducted among healthcare professionals working in tertiary hospitals. Data from 260 respondents were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS. The findings demonstrate a significant positive relationship between digital documentation systems and clinical workflow efficiency. User satisfaction partially mediates this relationship, while organizational IT support strengthens the effect of digital documentation on workflow efficiency. The model explains a substantial proportion of variance in workflow efficiency, highlighting the importance of both technological and organizational factors. The study contributes empirical evidence to health informatics literature and offers practical insights for healthcare administrators aiming to optimize digital documentation to improve clinical workflows.
Digital documentation systems; Clinical workflow efficiency; Electronic health records; User satisfaction
Effects of Pre and Post Workout Nutrition on Muscle Growth in University Students with Resistance Training in China
Background & Aim: There is a debate regarding pre and post workout meal impact on muscle building. The objective of this research study was to find best nutrient timing to get the maximum lean muscle mass and lose fat mass in University Students males with same type of resistance training. Methodology: The sample size was 20 male University Students s aged 18 to 55 who perform resistance training from selected fitness centers in Lahore. The participants were divided into 2 groups i.e. Group a (control group): who consume balance diet throughout the day, Group B (experimental group): who consume balance diet but focus on pre and post workout meal. A self-structured questionnaire was used for the comparative study, which included questions about age, weight, height, analysis of body fat and analysis of skeletal muscle strength. Results: Participants of both groups have nearly same reduction in body fat percentage but when in strength analysis the participant of control group have 19% improvement in 1 RPM while participants in experimental group have 40% improvement in 1RPM.Conclusion: The pre and post workout meal showed better effect on increase in skeletal muscle mass through resistance training as the results indicated that the experimental group have more increase in 1RPM strength than the control group
Meal Impact, Muscle Building, Balance Diet, Workout Meal, RPM
Consumption of Yogurt And Its Effect On Gut Health
Background & Aims: The benefits of probiotics for health have been known for a very long time. Probiotics promote gut health because they play a significant role in preventing certain illnesses and ailments. Probiotic yogurt is enjoyed widely all over the world. It is becoming more popular as a result of its benefits. Yogurt consumers had healthier nutritional habits and improved health than non-consumers. Yogurt provides several health advantages, including the prevention of digestive diseases, and the enhancement of the immune system. This study aims to compare the gut health of yogurt eaters with that of others. Methodology: The study sample size was 60 adults aged 18 to 35 years old from various areas of Lahore. The sample size was divided into two groups, i.e., yogurt eaters and non-yogurt eaters. A self-structured questionnaire was used for the comparative study, which included questions about weight, height, and age as well as the frequency and quantity of yogurt and yogurt-containing products consumed. Moreover, it also included the gastrointestinal symptoms rating scale, which was used for the gut health assessment of both groups. Results: Participants completed the entire questionnaire. Yogurt from local shops was preferred by 36% of the participants whereas, 37% preferred to make yogurt at home. Yogurt consumers prefer to have raita and plain yogurt among the yogurt and yogurt-containing products. Regardless of their yogurt consumption, the majority of the participants felt tired during the past week. Participants' gut health was evaluated using the scale. Conclusion: The study reveals that the majority of yogurt consumers have better gut health compared to non-yogurt consumers. Yogurt consumers have normal to mild symptoms of poor gut health whereas, non-yogurt consumers also showed severe symptoms of poor gut health. The majority of the non-yogurt consumers experienced heartburn, tummy pain, belly gurgling, and nausea for 2 days as well as they also experienced severe symptoms of diarrhea, constipation, and excess flatulence
Gut health, Probiotics, Yogurt and gut health, Comparison of yogurt and yogurt-consumers, Choices of yogurt, Diarrhea, Constipation, Gastrointestinal tract, GI complications
BENEFITS OF COCHLEAR IMPLANT IN TEEN AGERS: THE PARENTS PRESPECTIVE
Background and Objective: The language is the medium by which communicate and sound is the power source to understand the language these are our years and the hearing sense contact as with the word. The objective of this study is to analysis the subjective benefits of cochlear implantation in teen agers according to parents’ Prespective. Method: It was carried out in teen agers aged 13-19 years. A self-designed questionnaire was used to assess the potential point of view. The questionnaires were filled by parents included the following sections: hearing, communication and psychosocial. Decision to implant, process of implantation, positive effect of the implant, communication, supporting the child, self-reliance, well-being, happiness, social relationships, education, pre and postoperative services provided by the implant center. Parents of 50 Teen agers with congenital deafness or who became deaf between the age of 1 to 7 years and received cochlear implant were asked. Results: The results of present study, cochlear implant had a positive effect on the quality of life of teen agers and their families in different variety of ways. All things related to teen agers had improvement in quality of life after cochlear implant activation. Parents were more satisfied with the domains self-reliance, communication and social relations because it affects more on child’s life. Conclusion: Access to auditory information helped the Teen agers to join the hearing world and to learn to speak. From the parent’s perspective cochlear implant improves the quality of life of their Teen agers , especially when it comes to self-confidence, social relations and communication. Parents were more satisfied with their child’s overall performance.
Hearing impairment, Hearing loss, Cochlear implant, Communication, Psychosocial.
Comparative Effect of Inhaling Corticosteroids in Different Doses and Durations with risk of Diabetes Mellitus Type 2 in Chronic Asthma Patients at Mardan Medical Complex
The asthma phenotype can modify or amplify the colorful presumed processes for the elevated threat of T2DM among asthmatics. Because asthma is so diverse, researchers have long attempted to categorize it into discrete phenotypes and endotypes in an effort to better understand pathobiology and create specialized treatment choices. Asthma among individuals without dislike and evidence of atopic sensitization, referred to by the perfectly coined labels T2-low asthma, nonallergic asthma, or non-eosinophilic asthma, has consistently been associated, despite variations in research designs and cohorts. Piecemeal from standard antipathetic triggers, sweating to identify danger factors for severe and inadequately controlled asthma may be especially pertinent to these individualities. Recently, prediabetes and diabetes have been associated with an increased risk of adult asthma exacerbations. These conditions clinically manifest as fasting hyperglycemia and, in the case of type 2 diabetes, are characterized by hackneyed elevations in insulin resistance and systemic inflammation. The presence of comorbidities is crucial for the management and prognosis of asthma since it is linked with low quality life, increased healthcare utilization, and insufficient disease control. According to studies, people with asthma are more likely than controls to have comorbidities like type 2 diabetes. These conditions may also be linked to a worse prognosis for asthma, resulting in increased health resource utilization and a lower quality of life implants significantly enhanced their teen aggers’ quality of life, particularly in boosting self-esteem, improving social interactions, and facilitating communication.
Inhaling Corticosteroids, Doses, Diabetes Mellitus, Type 2, Chronic Asthma Patients,