Interpretive Approach Research Articles (Page 1)

To explore nurse managers' awareness and concerns about climate change, the factors influencing their perceptions, and their leadership roles in sustainable healthcare. Nurse managers play a pivotal role in promoting climate resilience and embedding sustainability into healthcare organizations. Their leadership is essential for mobilizing teams and aligning healthcare systems with sustainability goals, yet evidence on their awareness, concerns, and leadership in this area remains limited. A mixed-methods convergent parallel design was employed. Quantitative data were collected through surveys of nurse managers, and qualitative data were obtained from focus group discussions. Findings were analyzed using descriptive and interpretive approaches and integrated through the Climate Adaptation Framework. Nurse managers reported high awareness and concern regarding climate change. Awareness was shaped by a younger age, higher education, and prior climate training. Despite strong motivation, participants highlighted limited institutional understanding, weak policy frameworks, and resource constraints. Qualitative insights revealed fragmented planning and a pressing need for interprofessional collaboration. Advancing climate action requires shifting from individual efforts to system-level strategies. Nurse managers are positioned to inspire teams and foster innovation, but their effectiveness depends on stronger organizational commitment, adequate resources, and aligned policies. Although nurse managers show readiness to lead, structural barriers such as inadequate policies, funding, and training restrict sustainable action and underutilize their leadership potential. Embedding climate education into curricula, strengthening institutional sustainability initiatives, and fostering cross-sector collaborations are essential for leadership development. Policies should prioritize sustainability leadership training, allocate resources for adaptation, and provide structured frameworks that empower nurse managers as transformative leaders in climate-resilient healthcare systems.

The stability paradox: Why high prediction accuracy does not guarantee reliable feature importance in psychiatric research.

Predicting depression using serum perfluoroalkyl and polyfluoroalkyl substances levels via interpretable machine learning.

Enhanced UAV-based SPAD values estimation in tea chrysanthemum: an optimized and interpretable machine learning approach integrating spectral and textural information

Robust building energy retrofit evaluation under uncertainty: An interpretable machine learning approach

Interpretable two-stage machine learning approach for selecting suitable chemical treatment methods of fracturing flowback water and predicting pollutant removal

Nurses' perspectives on providing analgesia in the Italian prehospital emergency setting: A phenomenological study.

Climate change poses risks to water retention and carbon sequestration capacity in the source area of the Yangtze River.

The role of nursing communication: A critical interpretive synthesis.

Assessing Pavement Conditions Impact on Traffic Crashes: An Interpretable Machine-Learning Approach to Identify Crash Risk Factors and Performance Thresholds

Faculty-in-residence initiatives can face many challenges, but when successfully implemented, they can have a positive impact on the experiences of both students and faculty. This study explores how faculty serving as faculty-in-residence (FIR) at a public university in Southern California described their integration into residential communities, the challenges they encountered, and the impact of these roles on the broader faculty experience. Using a qualitative interpretive approach, we conducted in-depth interviews with eight FIRs to develop insights into the implementation of FIR programs. Our findings reveal that while the FIRs faced significant challenges—such as time constraints, low student participation, and differing visions of faculty and student affairs professionals—successful integration into residential communities led to meaningful faculty-student interactions and enhanced connection and engagement. The study concludes with recommendations for establishing successful FIR initiatives and emphasizing the role of personalized support, realistic program expectations, and strong collaboration among faculty and student affairs professionals.

This study contributes to the conceptualization of Customer Social Responsibility (CuSR) in the context of green financial product consumption, addressing a gap where the term has previously been embedded within corporate social responsibility (CSR) or brand strategy literature. Adopting an interpretivist qualitative approach, the research explores how Vietnamese Millennials—a tech-savvy, well-educated, and ethically aware generation—perceive and express CuSR. Data were collected through 20 in-depth interviews and five focus group discussions. The findings reveal four core dimensions of CuSR: essence, forms of expression, perceived values, and social impact. In addition, six influencing factors were identified: product characteristics, social influence, personal attributes, environmental and social awareness, regulatory environment, and corporate communication. The study contributes both theoretically and practically by clarifying the nature of CuSR, offering insight into consumer engagement with sustainable finance, and providing implications for green marketing and customer retention strategies in the digital era.

This study interrogates climate governance in the Southern Africa’s socio-ecological peripheries, concentrating on how decentralized adaptation policies shape rural livelihoods confronted with deepening climate hazards. The region’s ecosystems are worsening under climate stress, with smallholder farmers and forest-dependent communities already positioned at the social periphery bearing the brunt of more erratic precipitation and rising temperatures. The study utilized secondary materials, including peer-reviewed articles, official policy documents, and theoretical discussions on governance and adaptive responses. Data analysis was conducted through an interpretive and integrative approach, critically juxtaposing insights from distinct disciplinary repositories and constructing thematically coherent groupings. The study found that while decentralisation can enhance adaptive governance, its overall effectiveness hinges on bolstering cross-scale finance, capacity, and integration. The study further established that marginalized populations particularly women and youth continue to be underrepresented in decisional arenas, which undermines the equity of adaptation initiatives. The study concludes that decentralized climate governance can achieve transformation only when it is inclusive, sufficiently financed, and intricately linked to overarching rural development plans.

Language functions as a central catalyst in the creation, transmission, and advancement of knowledge, shaping educational systems and intellectual development across societies. This qualitative analytical study explores the Arabic language in comparison with other living languages, examining their structural, historical, and cognitive contributions to education. The analysis draws on thematic and interpretative approaches to investigate how linguistic features influence learning processes and intellectual growth. Findings reveal that Arabic’s root-based morphology, complex syntax, and extensive vocabulary enhance conceptual learning and analytical reasoning, enabling precise and expressive communication. The research emphasizes that linguistic qualities—such as **precision, expressiveness, and lexical diversity—**serve as powerful instruments for knowledge dissemination and cognitive development. Moreover, the study traces Arabic’s evolution from a regional dialect to a globally esteemed scholarly language, highlighting its pivotal role in transferring knowledge among civilizations. By linking linguistic development with human capital growth, the research advocates for educational policies and pedagogical frameworks that strengthen the role of language in shaping intellectual and socio-economic progress. Ultimately, it underscores that language—particularly Arabic—is a vital instrument for knowledge expansion, cultural preservation, and sustainable educational development in an increasingly interconnected world.

Language functions as a central catalyst in the creation, transmission, and advancement of knowledge, shaping educational systems and intellectual development across societies. This qualitative analytical study explores the Arabic language in comparison with other living languages, examining their structural, historical, and cognitive contributions to education. The analysis draws on thematic and interpretative approaches to investigate how linguistic features influence learning processes and intellectual growth. Findings reveal that Arabic’s root-based morphology, complex syntax, and extensive vocabulary enhance conceptual learning and analytical reasoning, enabling precise and expressive communication. The research emphasizes that linguistic qualities—such as **precision, expressiveness, and lexical diversity—**serve as powerful instruments for knowledge dissemination and cognitive development. Moreover, the study traces Arabic’s evolution from a regional dialect to a globally esteemed scholarly language, highlighting its pivotal role in transferring knowledge among civilizations. By linking linguistic development with human capital growth, the research advocates for educational policies and pedagogical frameworks that strengthen the role of language in shaping intellectual and socio-economic progress. Ultimately, it underscores that language—particularly Arabic—is a vital instrument for knowledge expansion, cultural preservation, and sustainable educational development in an increasingly interconnected world.

This study examines how faculty members in Arab higher education institutions experience and interpret destructive middle leadership within the context of Education 5.0. While this reform agenda emphasizes innovation, equity, and human-centered learning, participants described leadership practices that undermine collaboration, trust, and pedagogical creativity. Drawing on semi-structured interviews with ten faculty members (20 interviews across two iterative rounds) and analyzed through an interpretive qualitative approach, the study is guided by an integrated framework of destructive leadership, transformative learning, and distributed leadership. The analysis identifies three themes: (1) the weaponization of authority, (2) hierarchical entrenchment and institutional inertia, and (3) silencing dissent and erasing voice. Together, these patterns show that harmful leadership is not merely interpersonal but structurally embedded—sustained by performative reform discourse, bureaucratic saturation, and the routinization of exclusion. The study argues for re-conceptualizing middle leadership as an ethical, relational practice aligned with the humanistic commitments of Education 5.0, and outlines implications for leadership development, evaluative criteria, and shared governance.

This study examines the symbolism and local wisdom in the traditional ceremonies of the Jombang community, East Java, using Clifford Geertz's theory of cultural interpretation. The traditions studied include Grebeg Apem, Wiwit Kopi and Sedekah Bumi, Bersih Desa, Kenduren Wonosalam, and Kungkum Sinden. The research was conducted using a qualitative interpretive approach through library research and descriptive analysis of literary sources and cultural documentation. The results show that each tradition contains a system of symbols that represent religious, social, moral, and ecological values that exist in Javanese agrarian society. Grebeg Apem contains symbols of self-purification and social solidarity. Wiwit Kopi and Sedekah Bumi reflect gratitude for the harvest and respect for nature. Bersih Desa emphasizes collective awareness of social and spiritual balance. Kenduren Wonosalam displays an expression of togetherness and local identity through agricultural products. Meanwhile, Kungkum Sinden demonstrates the relationship between art, spirituality, and the preservation of ancestral heritage. Thus, all these traditions are a form of meaning system that integrates humans, culture and nature into a symbolic unity that confirms the identity of the Jombang people in the midst of modern social change.

The human microbiome is crucial for health regulation and disease progression, presenting a valuable opportunity for health state classification. Traditional microbiome-based classification relies on pre-trained machine learning (ML) or deep learning (DL) models, which typically focus on microbial distribution patterns, neglecting the underlying relationships between microbes. As a result, model performance can be significantly affected by data sparsity, misclassified features, or incomplete microbial profiles. To overcome these challenges, we introduce Phylo-Spec, a phylogeny-driven deep learning algorithm that integrates multi-aspect microbial information for improved status recognition. Phylo-Spec fuses convolutional features of microbes within a phylogenetic hierarchy via a bottom-up iteration and significantly alleviates the challenges due to sparse data and inaccurate profiling. Additionally, the model dynamically assigns unclassified species to virtual nodes on the phylogenetic tree based on higher-level taxonomy, minimizing interferences from unclassified species. Phylo-Spec also captures the feature importance via an information gain-based mechanism through the phylogenetic structure propagation, enhancing the interpretability of classification decisions. Phylo-Spec demonstrated superior efficacy in microbiome status classification across two in silico synthetic data sets that simulate the aforementioned cases, outperforming existing ML and DL methods. Validation with real-world metagenomic and amplicon data further confirmed the model's performance in multiple status classification, establishing a powerful framework for microbiome-based health state identification and microbe-disease association. The source code is available at https://github.com/qdu-bioinfo/Phylo-Spec.IMPORTANCEThe human microbiome profoundly influences health and disease, but current computational tools often overlook the evolutionary relationships among microbes, leading to incomplete or inaccurate interpretations of complex microbial data. Phylo-Spec provides a new way to understand the microbiome by combining microbial abundance, taxonomy, and phylogeny within a unified deep learning framework. This model not only improves the accuracy of health status classification but also highlights key microbial contributors linked to disease. By capturing both microbial diversity and evolutionary context, Phylo-Spec bridges the gap between bioinformatics and biological insight, offering a powerful and interpretable approach for advancing microbiome-based diagnostics and precision medicine.

Abstract: The study explored the perceived solutions to the sanitation challenges in George Compound in Lusaka, Zambia. The study objective was to assess the socio-economic situation of the beneficiaries of Household Sanitation Project in George Compound. The researcher adopted the social constructivist ontology, knowledge was co-constructed with participants through an interpretive phenomenological approach, and reflexive thematic analysis. The sample was selected purposively and determined using data saturation, ensuring rich, contextual insights from relevant stakeholders. The study revealed that poor sanitation affects men and women differently and it was further observed that women are more affected due to their biological nature, which limit their ability to use public spaces for relief, unlike men who can use open areas. The construction of toilets under the project helped address gender-specific concerns related to sanitation. Moreover, most study participants noted that in the past, cultural norms discriminated against women and favoured men. Similarly, the researcher disclosed that most study participants revealed that toilets constructed by LWSC significantly reduced the disease burden for both men and women. Furthermore, the study revealed that the company offered flexible payment options, making it easier for beneficiaries to access sanitation products. Theoretically, it corroborates systems theory by illustrating the interrelated roles of stakeholders in sustainable sanitation. For policy, the study emphasizes integrated, multi-stakeholder interventions informed by practical experiences. Practically, it highlights the need for community sensitization and engagement to ensure sustainability of sanitation innovations. Knowledge-wise, the study provides contextual evidence specific to George Compound, documenting challenges, innovations, and operational insights that can inform similar peri-urban settings. In conclusion, addressing sanitation challenges in peri-urban Lusaka requires a holistic, systems-oriented approach that integrates infrastructure, regulation, education, cultural socialization, and community participation to ensure equitable, sustainable, and effective on-site sanitation services.

DNA computing has emerged as a transformative paradigm for tackling computational problems at the molecular level, yet existing approaches remain constrained in algorithmic interpretability, efficiency, and scalability. Here we present a DNA-based decision tree system that modularly embeds classification rules into DNA strand displacement reaction cascades for interpretable decision-making across various configurations. It supports cascaded networks exceeding 10 layers, parallel computation of 13 decision trees in a Random Forest involving 333 strands, and multimode operation (linear/nonlinear, binary/multi-class, single/tandem trees), while maintaining low leakage, rapid signal propagation, and minimal computational elements. Coupled with a DNA-methylation sensing module, it translates biomarker profiles into molecular instructions for tree traversal, reproduces in-silico predictions and enables accurate disease subtype classification. The decision tree system represents an interpretable, scalable, and memory-efficient DNA computing approach and will open new avenues for programming intelligent molecular machines with broad applicability.