Global Landscape of the Attack of Predatory Journals in Oncology.

PURPOSE: Open-access publishing expanded opportunities to give visibility to research results but was accompanied by the proliferation of predatory journals (PJos) that offer expedited publishing but potentially compromise the integrity of research and peer review. To our knowledge, to date, there is no comprehensive global study on the impact of PJos in the field of oncology. MATERIALS AND METHODS: A 29 question-based cross-sectional survey was developed to explore knowledge and practices of predatory publishing and analyzed using descriptive statistics and binary logistic regression. RESULTS: Four hundred and twenty-six complete responses to the survey were reported. Almost half of the responders reported feeling pressure to publish from supervisors, institutions, and funding and regulatory agencies. The majority of authors were contacted by PJos through email solicitations (67.8%), with fewer using social networks (31%). In total, 13.4% of the responders confirmed past publications on PJo, convinced by fast editorial decision time, low article-processing charges, limited peer review, and for the promise of academic boost in short time. Over half of the participants were not aware of PJo detection tools. We developed a multivariable model to understand the determinants to publish in PJos, showing a significant correlation of practicing oncology in low- and middle-income countries (LMICs) and predatory publishing (odds ratio [OR], 2.02 [95% CI, 1.01 to 4.03]; P = .04). Having previous experience in academic publishing was not protective (OR, 3.81 [95% CI, 1.06 to 13.62]; P = .03). Suggestions for interventions included educational workshops, increasing awareness through social networks, enhanced research funding in LMICs, surveillance by supervisors, and implementation of institutional actions against responsible parties. CONCLUSION: The prevalence of predatory publishing poses an alarming problem in the field of oncology, globally. Our survey identified actionable risk factors that may contribute to vulnerability to PJos and inform guidance to enhance research capacity broadly.

Opportunities, challenges, and future directions for simulation modeling the effects of structural racism on cancer mortality in the United States: a scoping review.

PURPOSE: Structural racism could contribute to racial and ethnic disparities in cancer mortality via its broad effects on housing, economic opportunities, and health care. However, there has been limited focus on incorporating structural racism into simulation models designed to identify practice and policy strategies to support health equity. We reviewed studies evaluating structural racism and cancer mortality disparities to highlight opportunities, challenges, and future directions to capture this broad concept in simulation modeling research. METHODS: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review Extension guidelines. Articles published between 2018 and 2023 were searched including terms related to race, ethnicity, cancer-specific and all-cause mortality, and structural racism. We included studies evaluating the effects of structural racism on racial and ethnic disparities in cancer mortality in the United States. RESULTS: A total of 8345 articles were identified, and 183 articles were included. Studies used different measures, data sources, and methods. For example, in 20 studies, racial residential segregation, one component of structural racism, was measured by indices of dissimilarity, concentration at the extremes, redlining, or isolation. Data sources included cancer registries, claims, or institutional data linked to area-level metrics from the US census or historical mortgage data. Segregation was associated with worse survival. Nine studies were location specific, and the segregation measures were developed for Black, Hispanic, and White residents. CONCLUSIONS: A range of measures and data sources are available to capture the effects of structural racism. We provide a set of recommendations for best practices for modelers to consider when incorporating the effects of structural racism into simulation models.