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A scoping review of the research integrity architecture and how it is addressed in legal frameworks, institutional policies, and the scholarly literature: Research protocol.

Perez-Carreno, J. G.; Torres-Sarmiento, C.; Duarte Castro, A. F.; Gomez, L. E.; Bachelet, V. C.

2021-10-18 medical ethics
10.1101/2021.10.15.21265065 medRxiv
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BackgroundResearch integrity is a dynamic area within the ethical research ecosystem. Several efforts have been made to incorporate this topic in scientific governance frameworks. However, the efforts generally result in non-binding declarations and policies. Due to differences in legal systems, research cultures, and institutional approaches worldwide, there is a need to identify and map existent strategies on sound scientific practices. ObjectiveThis scoping review aims to systematically search, map, and evaluate the best available evidence on strategies and recommendations regarding research integrity. The goal is to identify international, national, regional, and local legal frameworks, institutional policies and guidelines, research integrity policies, interventions, strategies, and recommendations for: O_LIThe design and conduct of research projects, C_LIO_LIThe publication of research results, C_LIO_LIThe monitoring of scientific practices, C_LIO_LIThe implementation of corrective actions, and C_LIO_LIMentoring and education on research integrity. C_LI MethodsThe search will follow the PRISMA Extension for Scoping Reviews (PRISMA-ScR) and the methodological approach designed by Arksey and OMalley. It will include legal frameworks, national and international governmental and non-governmental documentation, and scholarly articles published in peer-reviewed journals on research integrity. The search will be conducted in PubMed/MEDLINE, Web of Science, JSTOR, Latin American & Caribbean Health Sciences Literature (Lilacs), Scopus, OECD Library. It will be complemented with hand searching and scanning, covering other databases and grey literature sources. We will extract and synthesize the data using two macro-genres: legal documents (soft law and hard law) and non-legal documents.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.