Background: Stigma associated with tuberculosis (TB) continues to undermine patient well-being, treatment adherence, and public health goals and objectives. This study aims to systematically review the literature to identify and synthesize TB stigma reduction interventions published between 2015 and 2025. Methods: Following the PRISMA guidelines, we conducted a comprehensive literature search across PubMed, Scopus, Science Direct, ProQuest, and Google Scholar. Eligible studies included those with qualitative, quantitative, and mixed-methods designs that focused on interventions related to TB-related stigma. We categorized the studies into three groups: (1) intervention development studies, (2) TB treatment programs with stigma reduction outcomes, (3) stigma-specific interventions. Data extraction and quality appraisal were conducted independently by two reviewers using the Mixed Methods Appraisal Tool (MMAT). Results: A total of 15 studies met the inclusion criteria. Five studies focused on co-developing stigma interventions, which incorporated multi-level and multicomponent strategies targeting internalized, enacted, anticipated, and intersectional stigma. Two studies assessed TB treatment-related interventions (e.g., home-based care, digital adherence tools) with incidental stigma reduction effects. The remaining seven studies implemented stigma-targeted interventions, including educational programs, video-based therapy, peer-led support, and anti-self-stigma toolkits. Interventions addressed stigma across individual, interpersonal, institutional, community, and policy levels. Conclusions: This review highlights the evolution and diversification of TB stigma interventions over the past decade. While earlier interventions emphasized education and support, recent strategies increasingly integrate peer leadership, digital platforms, and socio-ecological frameworks. The findings underscore the need for comprehensive, contextually grounded interventions that reflect the lived experiences of people affected by TB.

Objectives: This systematic review and meta-analysis evaluated differences in job satisfaction scores between nurses and physicians, examining variation by (a) care setting (hospital, emergency department, outpatient, mixed), and (b) time period (pre-COVID, during COVID, post-COVID). Methods: We systematically searched PubMed, Scopus, ScienceDirect, Web of Science, and CINAHL for studies published between January 2020 and July 2025. Eligible studies reported mean and standard deviation values for job satisfaction among physicians and nurses in healthcare settings across the specified timeframes. Studies were excluded if they assessed other types of satisfaction or combined data across COVID periods. Pooled standardized mean difference (SMD) was calculated using random-effects models in R. Results: Before COVID-19, the SMD was −2.40 (95% CI −8.05 to 3.26; I² = 98%). During the pandemic, the estimate was 1.39 (95% CI −0.57 to 3.35; I² = 91.5%), and post-pandemic, it remained small (SMD = 0.29; 95% CI −1.63 to 2.22; I² = 95.8%). Emergency care during COVID showed a significant advantage for physicians (SMD = 0.29; 95% CI 0.05 to 0.52; I² = 0%). Post-COVID, mixed settings slightly favored physicians (SMD = 0.06), while primary care favored nurses (SMD = −0.30); subgroup differences were significant. Conclusions: The findings reveal that job satisfaction is not solely determined by professional role but is significantly influenced by temporal and contextual factors. Job satisfaction is shaped more by temporal and contextual factors than by professional role. While no consistent differences were observed pre-pandemic, emergency care favored physicians during COVID, and post-pandemic trends showed modest advantages for nurses in primary care and physicians in mixed settings. Due to the methodological limitations of this meta-analysis, including high heterogeneity, reliance on cross-sectional data, and very low/low certainty of evidence, these results should be interpreted with caution.

Background/Objectives: Nurses and nursing students are central to HIV prevention and care, but studies of their general HIV-related knowledge, attitudes, and practices use heterogeneous populations, instruments, scoring systems, and thresholds. This review had three objectives: (1) to meta-analyze, separately, the prevalence of study-defined high/good general HIV knowledge, mean general knowledge scores, prevalence of study-defined positive/favorable general attitudes, and mean favorable-attitude scores; (2) to synthesize HIV-related clinical-practice findings narratively when measures were not comparable; and (3) to explore whether pooled knowledge and attitude estimates differed between practicing nurses and nursing students. General outcomes excluded narrowly focused PrEP-, PEP-, stigma-only, and item-level constructs. Methods: PubMed/MEDLINE, Scopus, PsycINFO, and CINAHL Complete were searched for eligible quantitative studies. Four knowledge and attitude outcomes were meta-analyzed in separate random-effects models; practice outcomes were not pooled. Heterogeneity, 95% prediction intervals, leave-one-out influence, possible small-study effects, subgroup differences, and certainty of evidence were assessed. Results: Thirty-four studies were included. The pooled prevalence of high/good HIV-related knowledge was 52.4% (95% CI 30.4–73.5%; I² = 97.5%; 95% prediction interval 3.6–97.0%), and the pooled mean knowledge score was 66.3% of the possible score (95% CI 60.8–71.8%; I² = 99.8%; prediction interval 40.0–92.5%). Positive/favorable attitudes had a pooled prevalence of 41.6% (95% CI 7.3–86.5%; I² = 99.1%; prediction interval 0.0–99.9%), and the pooled mean favorable-attitude score was 48.1% of the possible range (95% CI 41.9–54.2%; I² = 99.7%; prediction interval 21.3–74.8%). Thus, all four meta-analyses showed extreme heterogeneity and wide prediction intervals. Prespecified subgroup tests did not detect nurse-versus-student differences for either knowledge outcome or favorable-attitude prevalence; a higher pooled mean favorable-attitude score among students was observed in one exploratory comparison. Four studies reported non-equivalent practice indicators (16.0–85.3%), which were not pooled. Certainty was very low for all meta-analyzed outcomes. Conclusions: The pooled estimates should be interpreted as descriptive summaries across heterogeneous operationalizations rather than as precise global nursing-workforce benchmarks or common competency thresholds. More standardized measurement, objective assessment of clinical practice, and longitudinal and interventional research are needed.

Background: The rapid development of artificial intelligence (AI) and machine learning (ML) technologies has created new opportunities for improving healthcare systems worldwide. In Kazakhstan, national digitalization initiatives have supported the introduction of electronic health records and medical information systems; however, the level of AI readiness, development, and real-world implementation in healthcare remains insufficiently explored. Objective: This study aimed to synthesize current evidence on the readiness, development, and implementation of AI and ML technologies in the healthcare sector of Kazakhstan. Methods: A scoping review was conducted following the Arksey and O’Malley methodological framework and reported according to the PRISMA-ScR guidelines. The protocol of this scoping review was registered on the Open Science Framework (OSF). Electronic searches were performed in PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar for studies published between 2020 and 2025. Inclusion criteria covered empirical studies, policy analyses, and mixed-methods research focusing on AI in Kazakhstan’s healthcare context. Data were extracted using a standardized template and synthesized across three domains: AI readiness, AI development, and AI implementation. Results: A total of ten studies were included in the final synthesis. Evidence of AI readiness was mainly reported at the educational, professional, regulatory, and system levels, with limited workforce preparedness, insufficient formal training, and gaps in data protection and ethical regulation. AI development was primarily concentrated on technical model creation, including deep learning for medical imaging and automated laboratory interpretation, but often lacked clinical validation. Real-world AI implementation was reported in a small number of clinical settings, particularly in rehabilitation and laboratory medicine, where AI tools were reported to improve workflow efficiency, diagnostic support, and documentation processes. Conclusions: The Kazakhstan AI/ML healthcare literature is emerging and heterogeneous. While technical development and foundational digital infrastructure are advancing, evidence of routine clinical implementation remains limited, and readiness gaps persist in training, governance, interoperability, and regulatory oversight. Future research should prioritize implementation-focused evaluations, clinical validation, and governance models to support safe adoption across healthcare settings. Clinical trial number: Not applicable.

Tuberculosis (TB) remains a major public health challenge globally and in Kazakhstan, despite progress in reducing incidence. Increasing evidence highlights the role of social and structural determinants in shaping TB risk, disease progression, and treatment outcomes. However, limited research has explored how decision-makers conceptualize these factors and identify system-level gaps in TB control. This study aimed to examine regional decision-makers' perspectives on TB burden, social protection, health system capacity, and governance in Kazakhstan. A qualitative study was conducted using in-depth, semi-structured interviews with 12 decision-makers from six regions of Kazakhstan, including representatives of regional health authorities and local governance structures. Participants were purposively sampled to ensure diversity in institutional roles and geographic context. Interviews were conducted between August and December 2020, audio-recorded, transcribed verbatim, and analyzed using inductive codebook thematic analysis supported by ATLAS.ti software. The study followed COREQ guidelines. Six major themes were identified. Participants consistently framed TB as a condition closely linked to structural inequality rather than solely a biomedical issue. Risk was perceived as concentrated among socially and geographically vulnerable populations, particularly in rural areas with limited access to care. A key finding was the identification of a "biomedical-social gap," whereby strong diagnostic and treatment capacity was perceived as insufficiently connected to social protection, outreach, and governance mechanisms. Participants attributed this gap to fragmented social assistance, uneven support for transport, food, and income-related needs, workforce and infrastructure constraints, inequities in rural access, and insufficient institutionalization of NGO involvement. Sustaining political commitment and non-stigmatizing public awareness was also identified as important for long-term TB control. Regional decision-makers perceived TB control in Kazakhstan as constrained by fragmented linkages between biomedical services, social protection, NGO outreach, rural access support, and intersectoral governance mechanisms. Strengthening patient-centered social support and coordination across clinical, social, and civil society sectors may help reduce perceived fragmentation and support more equitable TB control.

Thyroid diseases arise from both internal and external factors. Changing environmental and radiation conditions are increasing their prevalence and altering thyroid disease patterns. This review highlights current insights into how environmental influences affect thyroid structure and function, outlining recent discoveries, mechanisms, and areas needing further research. The purpose of the study. To review scientific literature that investigates the effects of adverse industrial environmental factors on thyroid gland function. Materials and Methods. A search for relevant studies was conducted in electronic databases, including PubMed, Web of Science, and Scopus, using specific terms and keywords. Only original observational studies examining the impact of harmful environmental factors on the development of thyroid pathology were included. Results. High levels of heavy metals (Pb, Hg, Cd, As) are linked to increased thyroid cancer risk. In oil and gas regions, children exhibit elevated levels of manganese, boron, vanadium, and silicon in their hair, which can interfere with iodine uptake and lead to iodine deficiency. Cadmium lowers thyroid-stimulating hormone, contributing to hyperthyroidism. Radiation-exposed areas (e.g., Chernobyl, Semipalatinsk) show more nodules and autoimmune thyroiditis. Fine particles (PM2.5, PM10) increase the risk of thyroid cancer, and prenatal air pollution affects newborn T4 levels. Organic and inorganic pollutants disrupt thyroid hormone production and promote cancer. Conclusion. Environmental contaminants, including heavy metals, radiation exposure, industrial residues and pollutants, and endocrine-disrupting chemicals, play a significant role in the occurrence and progression of thyroid disorders. The rise in chronic diseases, including thyroid pathologies, serves as a marker of environmental pollution. Further research is needed to explore the mechanisms by which environmental factors affect thyroid function. It is essential to develop prevention and monitoring strategies, particularly in environmentally unfavorable regions.