Bu çalışmanın amacı; hekim ve hemşirelerin alturistik liderlik algıları, hekim hemşire iş birliğine eğilimleri ve lider üye etkileşimi düzeylerinin belirlenmesi; hekim ve hemşirelerin alturistik liderlik algılarının hekim hemşire iş birliğine olan eğilimleri ve lider üye etkileşimi düzeyleri üzerinde etkisinin olup olmadığının tespit edilmesidir. Bu kapsamda Ankara’da faaliyet gösteren bir vakıf üniversitesine bağlı eğitim araştırma hastanesinde görev yapmakta olan toplam 192 hekim ve hemşireden elde edilen veriler değerlendirilmiştir. Analizler sonucunda katılımcıların alturistik liderlik algıları ve lider üye etkileşimi düzeyleri orta seviyenin üzerinde; hekim hemşire iş birliğine yatkınlıkları ise yüksek düzeyde bulunmuştur. Yürütülen regresyon analizlerinden elde edilen bulgulara göre ise alturistik liderlik alt boyutlarının hekim hemşire iş birliği ve alt boyutları ile lider üye etkileşimi ve alt boyutları üzerinde istatistiksel olarak anlamlı ve pozitif yönde etkilere sahip olduğu belirlenmiştir. Araştırma bulgularından elde edilen sonuçlar doğrultusunda hekim hemşire iş birliği ve lider üye etkileşimi düzeylerini artırma amacında olan sağlık yöneticilerine alturistik liderlik davranışları sergilemeleri önerilmektedir.

Background/Objectives: Structural inequalities in access to healthcare persist even within systems that have achieved near-universal coverage, reflecting the enduring influence of social determinants of health on service utilization. This study examines horizontal inequity in rehabilitation and specialist care in Türkiye and investigates whether access inequality mediates the well-documented income–depression gradient. Methods: Analyzing the nationally representative 2022 Türkiye Health Survey (adults aged ≥15; N = 22,742), we employed Latent Profile Analysis (LPA) to construct people-centered, multidimensional bodily burden profiles, and assessed need-adjusted access using survey-weighted logistic regression, Erreygers-corrected concentration-index decomposition, Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), and restricted cubic splines, with measurement-invariance and classification-uncertainty sensitivity analyses. Statistical mediation was examined with natural-effect models and E-value sensitivity analysis. Results: Although the system demonstrated responsiveness to need—78.8% of the highest-burden profile accessed specialist services—only 13.7% of this same group reached dedicated physiotherapy or rehabilitation, revealing a profound structural bottleneck in care coordination for marginalized populations with the greatest functional impairment. A persistent pro-rich gradient was confirmed by an Erreygers-corrected concentration index of 0.058 (95% CI 0.043–0.072), driven additively by income and education. Access did not mediate the income–depression pathway (natural indirect effect OR 1.001, 95% CI 1.0003–1.002). Conclusions: The mental health burden of low income operates through pathways that equitable healthcare access alone cannot address. These findings call for macroeconomic and people-centered health system reforms—including direct physiotherapy access, transportation subsidies, and social protection interventions—to advance health equity in rehabilitation utilization.

In the evolving healthcare landscape, the adoption of Health Information System (HIS) is crucial due to the surge in chronic diseases globally. This chapter examines the key components of clinical HIS, emphasizing their role in enhancing healthcare delivery and patient care. Despite the potential benefits, the implementation of HIS faces challenges such as high costs, integration complexities, and data security concerns. However, the advantages of improved patient care, enhanced efficiency, and data-driven decisions highlight the indispensability of HIS in modern healthcare. Furthermore, this chapter presents a bibliometric analysis to assess the current research landscape on HIS. The analysis reveals trends, gaps, and the impact of HIS on healthcare delivery and patient outcomes. By providing a comprehensive overview of the components, benefits, challenges, and research landscape of clinical HIS, this chapter emphasizes the crucial role of these systems in transforming healthcare and improving patient care. Request access from your librarian to read this chapter's full text.

Migrant disappearances have become a critical humanitarian crisis on global scale. However, the lack of strong, consolidated data across irregular migration routes is a barrier to effective intervention. In this paper, we aim to employ a sociotechnical systems perspective to design an ethics-infused infrastructure. Our primary objective is to convert multimodal digital traces into actionable intelligence, thereby supporting search, identification, and policy accountability. To standardize the analysis of decentralized digital evidence, we define a typology of 32 trace types encompassing ten domains, categorized from geospatial markers to psychosocial signals. This classification is the basis for the modular architecture of five linked cores. We present a novel approach that combines entity resolution and spatiotemporal reconstruction with provenance tracking and explainable AI to support effective humanitarian action. These features enable robust early warning capabilities and provide decision support aligned with OCHA standards. The framework aims to facilitate compliance with FAIR data principles and international human rights standards through embedding cross-layer safeguards including dynamic consent, auditability and interoperability. This research provides a scalable, replicable method to validate digital evidence throughout the migration cycle. It bridges computational innovation and rights-based governance. Future work should focus on operational pilots to assess feasibility, triage efficiency, identification accuracy, and family notification protocols.

The disappearance of migrants is a global humanitarian crisis aggravated by structural data deficiencies across fragmented migration routes. This study adopts a sociotechnical systems perspective to develop an ethics-integrated infrastructure designed to convert multimodal digital traces into actionable intelligence for search, identification, and policy accountability. We propose a novel typology classifying 32 trace types across ten domains ranging from geospatial markers to psychosocial signals like emoji usage, facilitating the systematic analysis of decentralized digital evidence. This classification underpins a modular architecture comprising five interconnected cores: ingestion, preprocessing, evidence integration, AI, and analysis. The system enables entity resolution and spatiotemporal reconstruction while incorporating provenance tracking and explainable AI to deliver early warnings and OCHA-aligned decision support. By embedding stringent cross-layer safeguards including dynamic consent, auditability, and interoperability, the framework ensures adherence to FAIR data principles and international human rights standards. This research offers a scalable, replicable method for validating digital evidence across the migration cycle, providing a bridge between computational innovation and rights-based migration governance. Future work targets operational pilots to assess feasibility and impact on triage efficiency, identification accuracy, and family notification protocols.

Background/Objectives: Adolescent somatic symptoms such as headache, irritability, and sleep difficulties are common and closely tied to adolescent mental health, yet they are typically studied as a single dimension and as individual complaints detached from the psychosocial adversity surrounding the adolescent. Drawing on the nationally representative 2022 Türkiye Child Survey (n = 3523, ages 13–17), we examined whether these symptoms are patterned by the psychosocial risk and support surrounding the adolescent. Methods: Measurement models, multiple correspondence analysis, survey-weighted ordinal regression, a mixed graphical model, and machine-learning algorithms were applied in sequence. Results: Somatic symptoms were organized around a dominant general factor, alongside closely correlated physical and psychological dimensions that showed differential external associations. The three relational microsystems formed a coherent psychosocial adversity gradient along which somatic burden increased. The two dimensions were linked to different factors: peer victimization, a risk factor, and parental support, a protective factor, were associated primarily with psychological burden (odds ratios per standard deviation 1.84 and 0.88), whereas female sex and chronic illness were linked more strongly to physical burden (2.35 and 1.61); body mass index, income strain, and housing problems showed no independent associations. Regression, network, and machine-learning analyses converged on this dissociation while indicating modest individual-level predictability. Conclusions: Adolescent somatic symptoms are thus systematically patterned by psychosocial risk and support at the population level, an association that may inform monitoring and psychosocially informed assessment rather than individual prediction, and that requires longitudinal work to interpret directionally.