Emergency department (ED) overcrowding has necessitated more efficient triage processes. Traditional methods can struggle to keep up with increasing patient volumes, and interest in machine learning-based triage systems is increasing. However, the perspectives of emergency department professionals, who play a critical role in triage decision-making, are often overlooked in ML development. This qualitative study explores emergency department professionals’ perspectives on the potential for machine learning-based triage to enhance triage processes in emergency departments. Semi-structured interviews were conducted with 13 ED professionals (9 physicians, 4 nurses) from 6 hospitals in Istanbul. A grounded theory approach was used to analyze the data, identifying themes related to current triage challenges, attitudes toward machine learning-based triage, and suggestions for ML algorithm development. Three main themes emerged: (i) patient and public interaction with triage, (ii) technology and ML in triage, and (iii) triage processes and challenges. Healthcare professionals expressed optimism about the potential of machine learning-based triage but raised concerns about the accuracy of current technology and the need for ML models to integrate complex clinical judgments, particularly regarding pain assessment and patient behavior. While machine learning-based triage has the potential to significantly enhance ED triage, emergency department professionals’ experiential insights are crucial for the development of more accurate and usable ML models which focus on incorporating human expertise.

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.

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.