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Correcting the scientific record on abortion and mental health outcomes

Julia Littell and colleagues argue that better adherence to ethical standards for correction or retraction of unreliable publications is essential to avoid harmful effects on public policy, clinical practice, and public health The scientific community has contended with unreliable research for decades.12 Ideally, fatally flawed studies will be detected by peer reviewers and rejected by journal editors, but these processes are subjective, varied, and susceptible to error.34 Post-publication critiques can identify important flaws, and public debate can shed light on the nature of these problems, but these actions do not correct the scientific record. Thus, when authors are unwilling or unable to make sufficient corrections, published papers occasionally have to be retracted to correct the scientific record. Standards for correction and retraction have been established by the Committee on Publication Ethics (COPE),56 the International Committee of Medical Journal Editors (ICMJE),7 and

CDC Laboratory Recommendations for Syphilis Testing, United States, 2024

Code sharing and artificial intelligence can help decolonise public health modelling

Code sharing and artificial intelligence have potential to empower researchers from low and middle income countries to develop their use of public health modelling Steps towards democratisation of efforts in global health and capacity strengthening in low and middle income countries (LMICs) have made significant progress in recent years.1234 These efforts have empowered local researchers with the skills to collect, process, and analyse data, bridging the data generation gap between the global north and south. But glaring disparities remain in the realm of modelling and prediction studies, which are predominantly led by countries in the global north, often with limited transparency in sharing the underlying codes.5 This absence of mandates to share codes when publishing in journals exacerbates the challenge for researchers from LMICs, who are often located in the global south. Artificial intelligence (AI) and ethical code sharing practices can narrow this divide, empowering researchers

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