The Scientific Temper publishes papers from Science and Engineering, Cognitive Neuroscience and Psychology, Pharmacy and nursing, and other related multidisciplinary dimensions after a peer-review process. Plagiarism-free manuscripts following all international-standard ethical guidelines by authors are highly recommended. Globally competitive findings and original and innovative ideas are the key factors for the acceptance of manuscripts for publication in The Scientific Temper.
Prem Narayan Tripathi for BABURAM AND SONS
Cross-Border Healthcare Challenges and Implications for Universal Health Coverage in Mizoram, India
Universal Health Coverage (UHC) aims to ensure that all individuals have access to essential healthcare services without experiencing financial hardship. However, achieving UHC remains a significant challenge in geographically remote and border regions such as Mizoram, India. Mizoram shares international borders with Myanmar and Bangladesh, leading to cross-border migration and increased healthcare demands. This study examines the healthcare challenges associated with cross-border movement and their implications for achieving Universal Health Coverage in Mizoram. A qualitative research design was adopted using secondary data analysis from government reports, World Health Organization publications, and academic literature. Thematic analysis was used to identify key healthcare challenges affecting service accessibility and delivery. The findings reveal major challenges including healthcare infrastructure limitations, shortage of healthcare professionals, communication barriers due to linguistic differences, increased disease burden, and financial and administrative constraints. Cross-border populations often face difficulties in accessing healthcare services due to documentation issues and lack of policy clarity. Additionally, communication barriers between healthcare providers and patients contribute to misdiagnosis, poor treatment adherence, and reduced healthcare outcomes. These findings highlight the need for improved healthcare infrastructure, culturally sensitive healthcare delivery, cross-border health policies, and enhanced social work interventions. Strengthening healthcare systems in border regions is essential for achieving Universal Health Coverage and ensuring equitable healthcare access for all populations.
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Intellectual Property as Financial Collateral: Ethical Dimensions of Securitisation and Default Enforcement in India
In modern knowledge-driven economies, intangible assets like intellectual property (IP) form the bulk of business worth—typically 80-90%—but Indian financial institutions routinely overlook them in secured loan arrangements. Through doctrinal review of key laws including the SARFAESI Act 2002, IBC 2016, relevant IP legislation, and RBI guidelines, alongside comparisons with advanced systems in the US (UCC Article 9, Bowie Bonds), Singapore (IP Financing Scheme), and South Korea (Technology Guarantee Program), plus ethical scrutiny via distributive justice and human dignity principles, this study explores IP’s viability for collateral use, appraisal, and securitization amid borrower insolvency. In contrast, legal provisions support IP transfer, obstacles such as disjointed registries, inconsistent valuations, and limited institutional readiness block progress. International examples highlight success through cohesive systems, emphasising the urgency of integrated regulatory reforms and principled oversight to promote fair lending and broader economic participation.
Density Functional Theory calculations, Spectroscopic study, Reduced Density Gradient and molecular docking of 2-[3-(4-chlorophenyl)-5-(4-(propane-2-yl) phenyl-4, 5-dihydro-1H pyrozol-1-yl]-4-(nitrophenyl)-1, 3-thiazole
The 2-[3-(4-chlorophenyl)-5-(4-(propane-2-yl) phenyl-4,5-dihydro-1Hpyrozol-1-yl]-4-(nitrophenyl)-1,3-thiazole (CPDNT) was synthesized by reflux technique. FT-IR and FT-Ramanstudies attest to the presence of functional groups in the 2CPDNT molecule together with DFT method. Vibrational assignments were done using scaled quantum mechanical force fields. To assess the molecule’s chemical reactivity, the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) energy gap and quantum chemical descriptors were computed using the frontier orbital energies. A Molecular Electrostatic Potential (MEP) Map is a powerful means for visualizing the electron density distribution of a molecule, helping to identify its electrophilic and nucleophilic regions. Theoretical topology analysis (ELF, LOL) has been performed. A Reduced Density Gradient (RDG) was applied to examine weak interactions. To find the protein-ligand interaction and its lowest binding energy, the CPDNT molecule was docked to the active sites of 1FSE, 3VZX, 5MKD, and LIXM protein structures.
Institutionalizing Spirituality for Mental Wellbeing: Scope for Innovation in National Mental Health Policies
The current paper has explored the possibility of institutionalizing spirituality as a complementary factor in promoting better mental health in the national mental health policies, and the Indian healthcare setting in particular. The study was based on the fact that mental health does not only encompass clinical therapy, but also emotional, social, and spiritual aspects of wellbeing. The study used a mixed-methods design, a positivist research philosophy and deductive approach. The databases including PubMed, Google scholar, and NIH were used in search of peer-reviewed articles published between 2022 and 2026 and based on the PRISMA framework, second-level data were gathered. The structured survey was conducted with 50 participants (comprising healthcare professionals and patients) in order to collect primary data. The results showed that there was a high level of approving spirituality as a supportive tool to mental health, with a general awareness of the gaps in current institutional and policy models. Although the respondents supported integrated scientific-spiritual methods, they stressed that there was a need to regulate ethically and professionally and implement evidence-based practices. The paper accepted that institutionalized spirituality had the potential to enhance holistic mental health care provision and guide new policy formulation provided it was done responsibly. The study is relevant in current debate on culturally sensitive, inclusivity, and integrative mental health models in India.
Exploring Behavioural Dimensions of Organic Food Repeat Purchase Behaviour: An Exploratory Factor Analysis Among Indian Consumers
The present study aims to identify and validate the underlying behavioural dimensions influencing repeat purchase behaviour of organic food consumers. With increasing awareness of health and environmental sustainability, organic food consumption has grown significantly in recent years. However, consumer behaviour toward repeat purchasing remains complex and influenced by multiple behavioural and psychological factors. An Exploratory Factor Analysis (EFA) using Principal Components Analysis with Varimax rotation was conducted on 24 Likert-scale items measuring consumer attitudes and behavioural tendencies toward organic food consumption. The Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy and Bartlett’s Test of Sphericity were applied to assess data suitability for factor analysis. Factors with eigenvalues greater than 1.00 were extracted, and factor loadings of 0.45 or above were retained. Internal consistency reliability was assessed using Cronbach’s alpha. Six multidimensional behavioural factors emerged: attitude towards organic food, environmental concern, health consciousness, organic food labelling, purchase intention, and repeat purchase behaviour, explaining a significant portion of the total variance. Reliability coefficients ranged from 0.752 to 0.808, with the overall repeat purchase behaviour scale demonstrating strong internal consistency (α = 0.946). The findings support the multidimensional nature of consumer decision-making regarding organic food consumption. The validated scale provides a reliable instrument for examining behavioural patterns of repeat purchase behaviour and offers valuable insights for marketers, policymakers, and researchers interested in promoting sustainable food consumption.
Digital Campaigns and Behaviour Change Communication for Organ Donation
Organ donation remains a major public health concern in India due to the persistent gap between the demand for organs and their availability. Despite advancements in medical science, factors such as low awareness, cultural beliefs, misinformation, and fear continue to hinder voluntary organ donation. In recent years, digital campaigns and Behaviour Change Communication (BCC) strategies have emerged as effective approaches to influence public attitudes and promote organ donation. This study examines the role of digital platforms in promoting organ donation through structured BCC approaches. The study adopts a descriptive and analytical research design based on secondary data. Data were collected from national health reports, government portals such as NOTTO, peer-reviewed journals, campaign reports, and published literature on digital health communication. The collected information was analysed using thematic and trend analysis to understand patterns in awareness generation, attitude change, and donor registration behaviour. Findings suggest that digital campaigns using storytelling, testimonials, and myth-busting messages significantly improve awareness and positive attitudes toward organ donation. Social media platforms are particularly effective among younger populations. However, digital access gaps and limited regional language content restrict wider participation. The study highlights the need for culturally sensitive BCC strategies, stronger digital outreach, and integration of awareness campaigns with online donor registration systems.
Digital Health, Technology and Innovation in Nutrition Monitoring in Lunglei District, Mizoram
India’s adoption of digital health technologies has progressed through several phases, beginning with early telemedicine initiatives and institution-specific administrative systems, and gradually evolving into a comprehensive national digital health ecosystem. In Mizoram, the use of digital health technology dates back to the mid-2000s, with early telemedicine initiatives and administrative tracking systems. By 2023, the state had emerged as a national leader in implementing integrated digital health ecosystems. More recently, in September 2024, the Civil Hospital in Lunglei began using a Health Management Information System (HMIS) to support hospital-level digital health operations. Alongside these developments in digital health, nutrition has become an increasingly important component of public health policy in India. Nutrition monitoring is now recognized as a critical mechanism for tracking health outcomes, particularly among women and children, with the broader aim of achieving a malnutrition-free nation. In Mizoram, digital nutrition monitoring has been implemented through the POSHAN Tracker App, which has been in use since March 2021. The main objective of this study is to examine the impact of digital health technologies, specifically HMIS, and to assess innovation in nutrition monitoring through the POSHAN Tracker App. The study adopts a qualitative research methodology, using interviews as the primary method of data collection. A total of 20 participants (10 female and 10 male) were interviewed. The research was conducted in the southern region of Mizoram, with a focus on Lunglei district. The study aims to identify existing gaps in digital health and nutrition management systems and to highlight areas where improvements can strengthen the overall health system in Lunglei, Mizoram.
An early classification of Alzheimer’s Disease with deep Features using Advanced Deep Learning Method (Graph Convolutional Neural Networks)
Earlier and precise classification of Alzheimer’s Disease (AD) disease is one of the most urgent issues in neuroimaging and computational neuroscience. Traditional deep learning models like the Convolutional Neural Networks (CNNs), are useful in extracting features of medical images, but cannot often capture the intricate inter-regional interactions of the human brain. To find a solution to this constraint, this paper presents a sophisticated deep learning model based on Graph Convolutional Neural Networks (GCN) to detect and classify the Alzheimer Disease at an early stage. The method proposed creates a brain connectivity graph, each node of which corresponds to a region of interest (ROI) computed based on MRI data, and the edge corresponds to an anatomical or functional correlation between the regions. Deep feature representations are learned by using multiple layers of GCN, to learn both local and global topological statistics of disease progression. Experimental testing on the Open Access Series of Imaging Studies (OASIS) and transformed into a 4D format to a 2D format dataset shows that the proposed GCN-based algorithm is much more effective in improving the accuracy of the classification, in comparison to the traditional CNN and machine learning algorithms. The model is also biologically explanatory in that it points to significant areas of the brain that play a role in the prediction of diseases. This study has highlighted the possibility of the graph-based deep learning in improving early diagnosis and clinical decision making in the case of Alzheimer.
Gender-Inclusive Innovation in Industry: Menstrual Leave Policy as Institutional Reform in India
Gender-inclusive innovation requires institutional arrangements that recognise embodied labour realities while dismantling structural barriers to women’s full participation in industrial ecosystems. This article examines menstrual leave policy as a site of socio-legal innovation within India’s evolving labour governance framework, with specific reference to Karnataka’s 2025 cross-sector policy. Using doctrinal analysis, comparative policy mapping, and feminist institutional theory, the study interrogates whether menstrual leave constitutes transformative gender-responsive industrial reform or risks reinforcing biological essentialism in labour markets. Findings suggest that menstrual leave, when embedded within broader workplace inclusion strategies—such as anti-discrimination safeguards, infrastructural reforms, and monitoring frameworks—can enhance productivity, retention, and equity in labour-intensive and knowledge sectors. However, poorly designed policies may generate unintended labour-market distortions and stigma. The article advances a conceptual framework linking gender-responsive labour regulation to innovation systems theory and proposes measurable indicators for evaluating gender-inclusive industrial transformation. The study contributes to global debates on menstrual equity, care economy governance, and inclusive industrial policy by situating India’s experience within comparative international discourse.
Violence Against Healthcare Workers: Need for a Comprehensive Legislation
The COVID-19 pandemic exposed the persistent vulnerability of healthcare workers to violence, harassment, and institutional neglect in India. Despite temporary protections introduced through the Epidemic Diseases (Amendment) Ordinance, 2020 and the invocation of the National Security Act, 1980, the legal response remains fragmented and inadequate for long-term safeguarding of healthcare professionals. Judicial interventions, notably in Jerryl Banait v. Union of India, highlighted the constitutional obligation of the State to ensure security and dignity for medical personnel during public health crises. However, reliance on general penal provisions under the Indian Penal Code, 1860 fails to recognize violence against healthcare workers as a distinct category of offence requiring specialized treatment, graded punishments, and structured compensation mechanisms. The proposed “Healthcare Service Personnel and Clinical Establishments (Prohibition of Violence and Damage to Property) Bill, 2019” sought to fill this legislative vacuum but was not enacted, leaving systemic deficiencies unaddressed. This paper critically evaluates the existing statutory framework, judicial responses, and enforcement gaps, emphasizing the need for a comprehensive central legislation applicable beyond epidemic situations. It argues for clearly defined offences, proportionate penalties, mandatory FIR registration, institutional accountability, and a dynamic compensation model to restore confidence among healthcare providers. Additionally, the constitutional challenge posed by public health being a State subject is examined, suggesting recourse to national interest provisions for uniform protection. A robust and enforceable legal framework is essential to ensure deterrence, accountability, and respect for the invaluable services rendered by healthcare professionals.
Impact of In-Office Dry Eye Therapy on Symptom Relief and Tear Film in Patients with Evaporative Dry Eye Disease in a Primary Optometry Clinic
Evaporative dry eye disease (EDE), commonly associated with meibomian gland dysfunction (MGD), is characterized by tear film instability and visual disturbance. Conventional treatments provide temporary relief without addressing the underlying gland dysfunction. In-office dry eye therapy using controlled heat, Light Emitting Diode (LED+) with vibration has emerged as a potential treatment, though evidence from primary optometry clinics remains limited. This study evaluated the impact of in-office dry eye therapy on both subjective symptoms and objective parameters in patients with evaporative dry eye disease. This prospective pre- and post-interventional study included 49 patients diagnosed with evaporative dry eye disease. Baseline assessment included Tear Film Break-Up Time (TBUT), corneal staining, meibomian gland evaluation, and Ocular Surface Disease Index Questionnaire (OSDI-12) scoring. All patients underwent standardized in-office dry eye therapy, with a follow-up evaluation conducted after one month. Paired t-test was used to analyze the changes in OSDI-12 scores, and the Wilcoxon signed rank test was used to analyze the TBUT score comparison. A statistically significant (p<0.001) reduction of the OSDI-12 score was observed following intervention. TBUT values improved significantly post-therapy (p<0.001). Corneal staining showed improvement in 75% of patients with EDE, and qualitative improvement in meibomian gland function was noted in the majority of the participants. In-office dry eye therapy was significantly associated with improved tear film stability and reduced symptom severity in patients with evaporative type of dry eye disease.
Social Inequalities and Health Disparities among Scheduled Castes and Scheduled Tribes: A Gender and Income Perspective in Maharashtra
Health disparities among Scheduled Castes (SC) and Scheduled Tribes (ST) in India continue to reflect deep-rooted social inequalities shaped by caste, gender, and economic disadvantage. This paper examines social inequalities and health disparities among SC and ST populations in Maharashtra, with a specific focus on gender and income as intersecting determinants of health outcomes. Maharashtra, despite being one of India’s economically advanced states, exhibits significant intra-state inequalities, particularly in tribal and socially marginalized regions. Using an intersectional framework, the study draws upon secondary data from national health surveys, government reports, and existing literature to analyse variations in health indicators such as nutritional status, maternal and child health, morbidity patterns, and access to healthcare services. The findings indicate that SC and ST communities experience disproportionately poorer health outcomes compared to the general population, with women from low-income households facing the highest levels of vulnerability. Gender-based discrimination, low household income, occupational insecurity, geographic isolation (especially in tribal districts), and limited access to quality healthcare services collectively exacerbate health risks. The paper highlights notable disparities between SC and ST populations across rural, tribal, and urban settings in Maharashtra, emphasizing how income constraints and gender norms restrict healthcare utilization and health-seeking behaviour. The study underscores the need for equity-oriented public health policies, strengthened primary healthcare systems, and targeted interventions addressing the specific needs of SC and ST women and economically marginalized households. By situating health outcomes within broader social structures, this research contributes to a deeper understanding of caste- and gender-based health inequalities and offers insights for inclusive and socially just health planning in Maharashtra.
Professional Social Work Interventions in Healthcare: Safeguarding Patient Rights and Strengthening Grievance Redressal Systems
Healthcare systems are increasingly challenged to uphold patient rights, ensure transparency, and establish effective grievance redressal mechanisms in the face of complex institutional structures and power imbalances. Within this context, professional social workers play a critical role as advocates, mediators, and facilitators of patient-centred care. This paper examines professional social work interventions in healthcare settings with a focus on safeguarding patient rights and strengthening grievance redressal systems. Grounded in social work values of social justice, human dignity, and ethical practice, the study explores how professional social workers contribute to awareness creation, rights-based advocacy, transparent communication, and conflict resolution between patients, families, and healthcare providers. The paper highlights key intervention strategies such as patient counselling, informed consent facilitation, documentation support, ethical mediation, and institutional liaison, which collectively empower patients to voice concerns and seek redress without fear of discrimination or retaliation. Special emphasis is placed on the role of social workers in designing and operationalising accessible grievance redressal mechanisms, including patient help desks, grievance committees, and feedback systems within healthcare institutions. By ensuring procedural transparency and accountability, social workers help bridge gaps between policy frameworks and lived patient experiences. The paper also discusses challenges faced by social workers, including systemic constraints, role ambiguity, and limited institutional recognition, while emphasising the need for capacity building and policy integration. The study adopts a qualitative, conceptual approach drawing upon existing literature, professional practice frameworks, and healthcare ethics guidelines. It argues that professional social work interventions are indispensable for strengthening patient-centred healthcare delivery and institutional accountability. The paper concludes by advocating for the formal integration of professional social workers into healthcare governance structures to enhance patient rights protection, transparency, and responsive grievance redressal systems, thereby contributing to equitable and ethical healthcare outcomes.
An empirical investigation of Linguistic Errors in a corpus of sixteen doctoral theses submitted to CHARUSAT to improve lexical repertoire and quality of Academic Writing
ABSTRACT The academic writing is grounded in skillful and proficient incorporation of language-based systems which reinforce the expression of argumentative discourse, conceptual knowledge and discipline-oriented scholarly communication. The earlier scholarly investigations mainly focused on discourse-related patterns and grammatical systems and perceived lexical repertoire as ancillary constituents. Despite this, the systematic and structured utilization of lexical resources contributes substantially in developing semantic meaning and supporting logical coherence in research writing. The current empirical investigation probes vocabulary-related linguistic resources through the empirical evidence by evaluating language-oriented systematic patterns in the dataset of sixteen doctoral dissertations submitted at Charotar University of Science and Technology (CHARUSAT), Changa between 2016 and 2020. It concentrates on sentence-structural arrangement, discourse-structural lexical terms, grammatical correctness, and lexical appropriateness. By investigating the sixteen dissertations using error-based analyses and lenses, the inquiry evaluates lexical-semantic selections facilitating argumentative discourse, writer’s position and academic discourse logical coherence in research-oriented exchange. The research study in the next stage explores cognitive, conceptual, knowledge-centered, and discipline-focused components pertaining to lexis-related deployment coupled with the acknowledgement of empirical results and implications leading to influence the pedagogical interventions in designing a layout of the training program and strategizing refinement and enhancement. The present scholarly investigation points out the prominence of vocabulary-based expertise and capability in disciplinary textual discourse. It also suggests pedagogical implications in building and strengthening lexical resources understanding, discipline-specific linguistic systems and lexis-based formulaic sequences in higher-level research writing.
AI-Driven Predictive Waste Management with IoT-Enabled Monitoring for Smart Cities
With an increase of the world’s population and rapid urbanization, the amount of municipal solid waste also increases. Traditional waste management focuess on specific collection time tables, manual inspections, and basic algorithms, which creates inefficiencies in routing and increases the chances of overflowing the bins. These issues also do not increase response time and slow down routing. Current methods that use algorithms to resolve static shortest-path routing or basic rule-based scheduling lack the ability to adjust to real time changes that a smart system integrates. This system is the first of its kind to incorporate cloud technology with AI and IoT. The system begins with smart waste bins with ultrasonic and other environmental sensors that continuously transmit data to the cloud. The first of two algorithms, IntelliFillNet, is a novel method of processing unstructured data from a sensor stream, focusing on data cleaning and anomaly detection, along with spatiotemporal prediction of sensor fill levels to generate dynamic prioritization scores for bins and predict overflows in the near future. The second new algorithm, EcoRouteSync, incorporates outputs from IntelliFillNet and, through reinforcement learning, optimizes the real-time collection and routing of vehicles to minimize service delays and fuel costs. The whole processing pipeline is linear, from the acquisition of sensor data, through predictive analytics, to adaptive routing optimization. For example, in the experimental assessment, EcoRoutesync demonstrated predicted accuracy, minimized unnecessary collection trips and operational costs, and improved responsiveness over the Smart Bin Insights Dataset (available via Mendeley Data). This validates the proposed architecture’s effectiveness and scalability in the smart city waste management domain.
Human Rights Disclosure in the Indian Banking Sector
The scope and factors influencing human rights transparency in the Indian banking industry are investigated in this study in light of the Securities and Exchange Board of India’s (SEBI) mandated adoption of the Business Responsibility & Sustainability Reporting (BRSR) framework. Despite the banking sector’s influential role in socio-economic development, empirical data on its human rights transparency remain scarce. Based on stakeholder theory, the study uses a 25-point transparency index to assess disclosed information across four dimensions: employees, customers, communities, and value chain partners. A manual content analysis of reports from 32 banks listed on the National Stock Exchange of India (NSE) for the 2023-2024 financial year reveals a moderate average transparency score of 58.42%. Multivariate regression analysis reveals a strong positive correlation between the quality of financial reporting and bank size, internationalisation, and online presence. These findings suggest that large banks with a global presence and a strong internet presence are more aggressive in releasing information in order to reduce reputational risks and meet the needs of international investors. In contrast, financial performance and bank age have a limited influence, indicating that in India, financial transparency is primarily driven by regulatory compliance and external stakeholder pressure, rather than internal financial surpluses. This study highlights the need for uniform reporting requirements to increase the transparency of smaller domestic institutions, which is important information for investors and regulators.
Domestic Violence– ‘Unwanted Pregnancy and Unsafe Abortions’: Negotiating Life amid ‘Intimate-Partner Violence’
Unwanted pregnancies and unsafe abortions are closely linked to domestic violence, reflecting broader issues of gender inequality and the lack of reproductive autonomy for women in India. Domestic violence, often associated with physical or emotional abuse, also involves control over a woman’s reproductive choices. Many women are denied agency in family planning decisions due to coercion, manipulation, or outright refusal of autonomy by their male partners. Sociocultural, economic, patriarchal, and societal conventions, awell as regular physical and verbal abuse by intimate partners, frequently place women in a precarious condition which shows how “consent and coercion” govern their life. Women unable and unwilling to continue an unwanted pregnancy often resort to unsafe abortions due to fear of violence, social stigma, and lack of access to safe healthcare, severely impacting their physical and psychological well-being. Therefore, drawing upon 20 In-depth interviews conducted in the Moradabad district of Uttar Pradesh, India, and the use of Gramsci’s theorization of ‘common sense’ this article explores male partners influence or control on reproductive decisions– refusing contraception, coercing pregnancies which results into leaving women powerless over their reproductive health. Further the article also attempts to investigate the nexus between domestic violence, unwanted pregnancies, and unsafe abortion practices. Besides, the article also examines women’s everyday negotiation and navigation strategy to overcome various forms of domestic violence including unwanted pregnancies. Further the article also tries to offer valuable insights and suggestion for policymakers, social workers, and healthcare workers to provide inclusive policy for dignified intervention in such crucial issues.
Work-Life Balance, Mental Health, and Sustainable Innovation: A Study of Women in Industry
Increasing female participation in modern economies highlights the critical intersection of work-life balance (WLB) and psychological well-being. This conceptual study examines WLB as a determinant of mental health and positions employee well-being as a strategic pathway toward SDG 9 (Industry, Innovation, and Infrastructure). While existing research often treats these variables in isolation, this paper integrates them to explore their collective impact on organizational sustainability. Drawing on Role Conflict, Conservation of Resources, Work-Family Border, and Human Capital theories, the study develops a framework explaining how workplace pressures and socio-cultural expectations influence innovative capacity. Through a thematic synthesis of global and Indian literature, the analysis identifies key organizational determinants of women’s mental health. Findings suggest that gender-responsive practices, flexibility, and institutional support mechanisms enhance both individual well-being and long-term industrial resilience. Ultimately, the study conceptualizes mental health as a vital resource for sustainable industrial development and innovation performance.
Policy to Practice: A Qualitative Study of Experiences of Ayushman Card Beneficiaries in India
Ayushman Bharat, Pradhan Mantri Jan Arogya Yojana (PM-JAY) is a flagship health insurance initiative of the Government of India aimed at advancing universal health coverage for economically vulnerable populations. While the scheme offers substantial financial protection, limited evidence exists on how beneficiaries experience its implementation in everyday healthcare settings. This study examines the lived experiences and challenges faced by Ayushman Card users in accessing healthcare services in urban slum communities of Vadodara, Gujarat. A qualitative exploratory approach was used. Data were collected through one-on-one interviews and case-based narratives with ten beneficiaries from the Mujhmoda and Ramnagar slum areas. The study focused on understanding barriers encountered during enrollment, verification, and service utilization. The findings indicate a significant gap between policy intent and service delivery. Beneficiaries reported low awareness of scheme features, lack of required documentation, dependence on middlemen and agents, and unofficial payments demanded by hospitals despite insurance coverage. Aadhaar based KYC and biometric verification created serious obstacles for elderly persons and individuals with disabilities. Migration and address inconsistencies further increased reliance on agents, leading to card errors and access denial. The study concludes that although Ayushman Bharat is comprehensive in design, systemic and administrative barriers undermine its effectiveness in practice. Enhancing beneficiary awareness, simplifying verification procedures, strengthening hospital accountability, and providing local facilitation support are essential for ensuring equitable and inclusive access to healthcare services.
Resource Translation Under Constraint: A Conditional Process Model of Women’s Subjective Career Success
Over the past few decades, women’s participation in the workforce has increased significantly across both developed and developing economies. Despite this progress, women remain underrepresented in senior leadership and executive positions. This persistent gap suggests that access to employment does not automatically translate into career advancement. Researchers increasingly argue that career progression depends not only on individual capabilities but also on the ability to access and mobilise organisational and relational resources. Although the number of women employed has increased, there continues to be a lack of women in leadership and obstacles to their professional growth. Current research has focused on family responsibilities, mentoring and perceived organisational support (POS) as independent predictors of career success; however, additive models do not adequately account for how organisational climates become subjective career success when there are structural barriers to providing caregiving. Using Social Cognitive Theory, Conservation of Resources Theory, and Social Exchange Theory, this research study proposes a conditional process model that explains women’s subjective career success as resource mobilization. Research study aims at analysing women’s subjective career success as the mobilisation of organisational, relational, and psychological resources, including mentoring support, institutional backing, and professional self-efficacy. Mentoring is positioned within this model as the relational channel through which POS is transferred into psychological and developmental resources; whereas, familial obligations function as a structural barrier to this resource transfer. The model improves the theoretical framework by differentiating between psychological efficacy and Structured Ability to effect or enact the resources. This reframing pushes beyond additive models of predictors towards explaining gender differences in career progression as a process. Thus, providing a rationale for why mentoring has different outcomes for advancement in women under conditions of different levels of caregiving responsibility. By reconceptualising women’s career success as cumbersome resource mobility rather than as individual aspiration against a block, this theory provides authority to explain the discord between gendered distributions of career paths developed.
The Structural Transformation of the Indian Health Insurance Ecosystem: A Comprehensive Analysis
This study examines the structural transformation of the Indian health insurance sector over the decade 2015–16 to 2024–25, assessing its role in advancing financial protection and Universal Health Coverage (UHC). Using secondary data from regulatory reports, National Health Accounts, financial inclusion indices, and industry publications, the study analyzes trends in premium growth, policy issuance, population penetration, claim settlement performance, out-of-pocket expenditure (OOPE), financial inclusion, and macroeconomic indicators. The findings indicate a nearly fivefold increase in total health insurance premiums and a rise in population coverage from 27.13% to 40.25% over the study period. Concurrently, OOPE as a share of Total Health Expenditure declined substantially from 60.6% to a projected 36%, reflecting increased public health expenditure and insurance expansion. However, the persistence of a sub-76% claim settlement ratio in the post-pandemic period highlights operational and trust deficits within the system. The analysis further demonstrates a strong correlation between rising per capita income, digital financial inclusion, and insurance uptake. While the sector has made measurable progress toward financial risk protection, significant gaps remain in informal sector inclusion, rural outreach, and claims transparency. The study concludes that sustained regulatory reform, digital infrastructure integration, and micro-insurance innovation are essential to achieving the “Insurance for All by 2047” objective and ensuring equitable health financing in India.
An accurate Prediction and Classification of early Alzheimer’s Diseases using Machine Learning Algorithm
Alzheimer’s disease (AD) is a progressive neurodegenerative disease that severely impairs memory, cognition and everyday functions. Early and accurate diagnosis of AD is important for timely clinical management but the conventional diagnostic protocols still suffer from subjectivity and high cost. In this study we propose a deep learning architecture for early AD prediction and classification based on Convolutional Neural Networks (CNNs). We trained the model using medical imaging data, specifically Magnetic Resonance Imaging (MRI), to automatically learn hierarchical spatial features indicative of the early AD brain subtly changing structure. Unlike traditional machine learning, the CNN uses convolutional layers, pooling, and fully connected layers to replace the need for handcrafted feature extraction to derive end-to-end learning. The proposed method is integrated with regularization and data augmentation to prevent overfitting and obtain robustness to different patient samples. Experimental evaluation results demonstrate that the CNN-based model possesses greater accuracy, sensitivity, and specificity than conventional classifiers and is able to accurately distinguish normal controls, patients with mild cognitive impairment, and patients with Alzheimer’s. This work shows the potential of CNNs as a valuable tool in computer Aided diagnosis (CAD) in the field of neurodegenerative diseases, contributing to the system of earlier diagnosis and clinical decision-making in their management.
Assessing Profitability, financial efficiency and Solvency: Financial Statement Analysis with special reference to ONGC
Analysis and interpretation of financial statements is another name for financial statement analysis. It creates significant connections between different components of the two financial statements, particularly the position and income statements. It assesses one’s financial stability and flaws in the company. Financial statement analysis is useful for gauging an organization’s profitability, liquidity, efficiency, and solvency. The examination of financial statements is of interest to all parties involved. Understanding and diagnosing financial statement data, as well as assessing the business’s profitability and financial strengths, are the primary goals of financial statement analysis. The objective of this paper is to analyse the financial stability of the company and the study covered 3 years from 2023 to 2025 including secondary data. Researcher has used comparative statement and Common size statement method of financial analysis. For the financial analysis, the researcher utilized a paired t-test using two samples for the mean in which he has taken two variables net profit and net worth. The company’s overall financial performance is steady, with good profitability and moderate solvency, even though continued debt and asset utilization optimization will further improve financial stability and long-term sustainability.
Educational Reforms for Integrated Health and Social Care: A Critical Review
Accelerated socio-economic changes, arising energy challenges, and growing well-being inequities emphasize the need for reforms in health, community health, and social work education. This paper investigates the nature and importance of educational reforms proposed to evolve competent, ethical, and philosophically responsive health professionals. It critically examines curriculum restructuring, competency-based instruction, integrative interdisciplinary learning, and community-familiarized training approaches selected across health, public health, and social service disciplines. The study emphasizes unifying field-located learning, digital technologies, research integration, and ethical practice to bridge theory-practice gaps. It debates aligning educational programs with national health priorities and global community health standards. The paper argues cooperative interprofessional education enhances collaboration, cultural competence, and critical skills for future experts. Sustained reforms are essential for revitalizing health systems, reconstructing service delivery, and addressing complex social and health issues in an ever-changing world.
STDO: Siberian Tiger and Devil Optimization — A Novel Hybrid Metaheuristic Algorithm for Energy-Efficient Task Scheduling in Cloud Computing
Energy-efficient task scheduling has emerged as a critical research challenge in cloud computing due to the exponential growth of data centres and associated energy consumption. Existing metaheuristic algorithms such as Particle Swarm Optimization (PSO), Tasmanian Devil Optimization (TDO), and Siberian Tiger Optimization (STO) suffer from limitations including premature convergence, excessive exploration, and stagnation in complex search spaces. This study proposes a hybrid algorithm, Siberian Tiger and Devil Optimization (STDO), which integrates exploration and exploitation mechanisms through a phased switching strategy and a persistent elite archive. The proposed method is evaluated across twelve heterogeneous cloud configurations with varying virtual machine capacities and task loads. Each experiment is conducted over multiple independent runs to ensure statistical reliability. The results demonstrate that STDO achieves superior energy efficiency compared to baseline algorithms while maintaining competitive makespan performance. Statistical validation using the Wilcoxon signed-rank test confirms the significance of improvements. The findings establish that hybrid metaheuristic approaches can effectively enhance scheduling performance in cloud environments while ensuring scalability and robustness. STDO was evaluated in comparison to TDO, STO, and PSO in 12 cloud configurations. These configurations included three VM pool sizes (5, 10, and 20 VMs) and four task levels (50 to 200 tasks). With an average of 0.024456 MI/Watts, STDO outperformed STO and PSO in every configuration, outperforming TDO by 12.0%, STO by 6.0%, and PSO by 23.9% in terms of energy efficiency. On scheduling time, it reduced makespan by up to 33.47% over PSO and 30.79% over TDO in constrained settings. It also held up far better as task loads scaled up, where PSO degraded by as much as 29% and STDO remained comparatively stable.
Development of an Ayurveda-Integrated Feature Engineering Framework for Disease Prediction
A combination of the conventional Ayurvedic diagnostic knowledge and the recent computational intelligence should provide a direction to an improved way of improving the accuracy of diagnosing diseases and broadening the horizon of the entire healthcare provision. This paper introduces an Ayurveda-Based Feature Engineering (AFE) Framework in disease prediction with the assistance of machine-learning techniques. The systematically Ayurvedic diagnostic parameters of the Ayurvedic Prakriti constitution, Dosha imbalance, Agni condition, Nadi, and Astavidha Pariksha are systematically translated into structured machine-readable numerical features. To create a high-quality set of features that was consistent with the traditional medical reasoning and data science demands, a dataset gathered in the Ayurvedic hospitals and clinics was annotated with these parameters encoded. Several machine learning classifiers such as the random forest (RF), the support vector machine (SVM) as well as the navie bayes (NB) were trained and optimized using this improved dataset. Experiments indicate that using Ayurveda elements of diagnoses leads to a significant increase in predictive performance over traditional symptom-only models with significant improvements in accuracy, F1-score, and AUC measures. The presented AFE framework contributes to a powerful bridge between Ayurveda classical and contemporary predictive analytics and makes it possible to implement culturally-rooted and predictable disease-based forecasting systems. The contribution provides the foundation of the future study on integrative healthcare analytics and provides a scalable framework of building more sophisticated Ayurveda-informed clinical decision support systems.
Legal Rights and Freedom of Healthcare Professionals against Violence: Comparative Analysis among India, U.S.A and United Kingdom
Rights are the interest which law protects by imposing duties on other persons. Any form of violence against healthcare professional is abuse of law, violation of constitutional rights and freedom. Physical and verbal violence are very often faced by healthcare professionals while delivering medical services. The present study attempts to investigates and compare the statutory acts, policies, procedures and historical judicial dicta as applicable for enriching the rights of healthcare professionals against violence in India, U.S.A and United Kingdom. Study is collectively based upon legal analysis, empirical evidence and qualitative research. The comparative study develops based upon evaluating and exploring rights which are commonly cherished by the healthcare professionals while delivering medical services. The study attempts to consider and analyze legal rights and further reviews its protection framework. The paper finally states suggestive methods to condense the violence faced by healthcare professionals so to enrich the rights as granted to healthcare professionals. The findings suggest medical institutes should work in collaboration with government agencies and vice-versa to minimize all form of violence against healthcare sectors. State shall be aggressive in making public awareness campaign reference to stop all form of violence towards healthcare professionals. Legal fraternity should take all necessary measure to stop the encroachment of perpetrators who are actively violating rights delegated to healthcare professionals.
Ensemble and Multimodal Approaches for Analyzing Student Engagement and Flexibility in Online Learning: A Review
The rapid growth of online learning environments has intensified the need to understand student engagement in order to enhance learning effectiveness and academic outcomes. Student engagement is typically inferred from digital traces such as login frequency, video viewing behavior, discussion participation, and assessment performance. However, another critical dimension of online learning is student flexibility, encompassing time management, self-regulation, and adaptability to learning schedules has received comparatively limited attention in existing research. This review paper systematically examines recent studies on student engagement analysis, with a particular focus on data modalities, Machine Learning (ML) and Deep Learning (DL) models, ensemble learning techniques, and multimodal learning strategies. The surveyed literature reveals extensive use of traditional ML algorithms and deep neural networks, often relying on single-source data and standalone models. While these approaches demonstrate promising predictive performance, they frequently fail to capture the complex and dynamic nature of student behavior in online settings. Recent trends indicate a growing interest in ensemble learning methods, which integrate multiple models to enhance prediction accuracy, robustness, and generalizability. Despite this progress, the review identifies significant research gaps, including the limited incorporation of flexibility-related features and the absence of comprehensive ensemble-based multimodal frameworks that jointly model engagement and flexibility. Based on the analysis, this paper argues that integrating engagement indicators with flexibility attributes through ensemble and multimodal learning approaches can provide a more holistic and reliable understanding of student behavior. Such frameworks have the potential to support early intervention, personalized learning, and improved decision-making in online education systems.