Healthcare

Background/Objectives: Burnout, caused by chronic workplace stress, encompasses emotional exhaustion, depersonalization, and low personal accomplishment. Over half of healthcare professionals experience burnout, leading to increased turnover. Third-wave cognitive behavioral therapies are effective in managing burnout. This study aimed to synthesize existing evidence on third-wave cognitive behavioral therapies and evaluate its effectiveness in reducing burnout among healthcare professionals. Methods: We systematically searched eight databases for studies published through May 2024. The eligible studies included randomized controlled trials and quasi-experimental designs evaluating third-wave cognitive behavioral therapies on burnout in healthcare professionals. A meta-analysis was conducted by applying fixed- and random-effects models. Results: A total of 5005 records were identified, of which 29 were reviewed and summarized, and 11 were included in the meta-analysis. Most interventions utilized mindfulness-based techniques (n = 26) with delivery methods including on-site (n = 16), online (n = 12), and hybrid (n = 1) formats. Third-wave cognitive behavioral therapies significantly reduced emotional exhaustion (standardized mean difference [SMD] = −0.686, confidence interval [CI] = −1.237, −0.136, p = 0.0145, I² = 92.5%) and depersonalization (SMD = −0.529, CI = −0.975, −0.083, p = 0.0202, I² = 89.3%), but the effect on low personal accomplishment was not statistically significant (SMD = 0.311, 95% CI = −0.319 to 0.941, p = 0.3338, I² = 89.4%). Conclusions: Third-wave cognitive behavioral therapies should be implemented to reduce emotional exhaustion and depersonalization; future research should target low personal accomplishment and explore approaches beyond mindfulness.

The early differentiation of benign choroidal naevi from malignant melanoma remains one of the most nuanced challenges in ophthalmic oncology, with profound implications for patient survival. Conventional diagnostic pathways rely on multimodal imaging and expert interpretation, but inter-observer variability and the rarity of melanoma limit timely and consistent detection. Recent advances in artificial intelligence (AI) offer a promising adjunct to conventional ophthalmic practice. This review provides a critical comparative synthesis of the studies to-date which have looked at AI’s use in the detection, risk stratification, and longitudinal monitoring of choroidal melanoma. While early results are promising—with some models achieving an accuracy comparable to expert clinicians—significant challenges remain regarding generalisability, dataset bias, interpretability, and real-world deployment. We conclude by outlining practical priorities for future research to ensure that AI becomes a safe, effective, and equitable tool for improving patient outcomes.

Background/Objectives: The coronavirus disease 2019 (COVID-19) pandemic imposed an unprecedented global health and economic burden, prompting rapid implementation of diverse public health interventions. This review aimed to synthesize global evidence on the cost-effectiveness of key COVID-19 control strategies, including vaccination, testing, and social distancing and to identify methodological, contextual, and equity-related determinants of their economic value. Methods: A narrative literature review was conducted using peer-reviewed studies published between January 2020 and September 2025 and indexed in PubMed, Scopus, and Web of Science. Eligible studies included economic evaluations and modeling analyses addressing COVID-19 interventions in healthcare, community, or educational settings. Data on costs, outcomes, and methodological features were extracted and synthesized descriptively. Results: Across 74 included studies, vaccination—particularly with messenger RNA (mRNA) platforms—emerged as the most cost-effective intervention across all settings, often cost-saving among high-risk populations. Combined or layered strategies integrating vaccination, testing, and selective social distancing consistently outperformed single interventions in both health and economic outcomes. Early and targeted implementation yielded the highest cost-effectiveness by preventing exponential transmission and healthcare overload. However, heterogeneity in modeling assumptions, analytic perspectives, and outcome measures limited comparability. Few studies applied extended or distributional cost-effectiveness frameworks to address equity, while indirect and long-term effects such as productivity losses and “long COVID” were frequently omitted. Conclusions: COVID-19 interventions are most efficient when early, targeted, and adaptive to local epidemiologic conditions. Integrating equity, methodological consistency, and broader societal impacts into future evaluations will strengthen evidence-based, economically sustainable pandemic preparedness and response strategies.