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This study developed, calibrated, and evaluated a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews. A machine learning classifier for retrieving randomized controlled trials (RCTs) was developed (the “Cochrane RCT Classifier”), with the algorithm trained using a data set of title–abstract records from Embase, manually labeled by the Cochrane Crowd. The classifier was then calibrated using a further data set of similar records manually labeled by the Clinical Hedges team, aiming for 99% recall. Finally, the recall of the calibrated classifier was evaluated using records of RCTs included in Cochrane Reviews that had abstracts of sufficient length to allow machine classification. The Cochrane RCT Classifier was trained using 280,620 records (20,454 of which reported RCTs). A classification threshold was set using 49,025 calibration records (1,587 of which reported RCTs), and our bootstrap validation found the classifier had recall of 0.99 (95% confidence interval 0.98–0.99) and precision of 0.08 (95% confidence interval 0.06–0.12) in this data set. The final, calibrated RCT classifier correctly retrieved 43,783 (99.5%) of 44,007 RCTs included in Cochrane Reviews but missed 224 (0.5%). Older records were more likely to be missed than those more recently published. The Cochrane RCT Classifier can reduce manual study identification workload for Cochrane Reviews, with a very low and acceptable risk of missing eligible RCTs. This classifier now forms part of the Evidence Pipeline, an integrated workflow deployed within Cochrane to help improve the efficiency of the study identification processes that support systematic review production. •Systematic review processes need to become more efficient.•Machine learning is sufficiently mature for real-world use.•A machine learning classifier was built using data from Cochrane Crowd.•It was calibrated to achieve very high recall.•It is now live and in use in Cochrane review production systems.

For over two decades, the World Organisation for Animal Health (OIE) has engaged in combatting antimicrobial resistance (AMR) through a One Health approach. Monitoring of antimicrobial use (AMU) is an important source of information that together with surveillance of AMR can be used for the assessment and management of risks related to AMR. In the framework of the Global Action Plan on AMR, the OIE has built a global database on antimicrobial agents intended for use in animals, supported by the Tripartite (World Health Organization (WHO), Food and Agriculture Organization of the United Nations (FAO) and OIE) collaboration. The OIE launched its first annual data collection in 2015 and published the Report in 2016. The second Report, published in 2017, introduced a new methodology to report quantitative data in the context of relevant animal populations, and included for the first time an annual analysis of antimicrobial quantities adjusted for animal biomass on a global and regional level. A continuing annual increase of countries participating in the data collection demonstrates the countries engagement for the global development of monitoring and surveillance systems in line with OIE international standards. Where countries are not yet able to contribute their quantitative data, their reports also highlight the barriers that impede them in data collection, analysis and/or reporting. The OIE Reports show annual global and regional estimates of antimicrobial agents intended for use in animals adjusted for animal biomass, as represented by the quantitative data reported by countries to the OIE. The OIE advises caution in interpretation of estimates made in the first few years of reporting recognizing some important limitations faced by countries as they develop their monitoring systems. The OIE remains strongly committed to supporting its Members in developing robust and transparent measurement and reporting mechanisms for AMU.

Background Interest in mixed methods methodology within medical education research has seen a notable increase in the past two decades, yet its utilization remains less prominent compared to quantitative methods. This study aimed to investigate the application and integration of mixed methods methodology in medical education research, with a specific focus on researchers’ perceptions, strategies, and readiness, including the necessary skills and expertise. This study adheres to the COREQ guidelines for reporting qualitative research. Methods Faculty members from King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Saudi Arabia, across its three campuses in Riyadh, Jeddah, and Al Ahsa, participated in this study during the 2021–2022 academic year. We conducted 15 in-depth, one-on-one interviews with researchers who had previously used mixed methods in their medical education research. Theoretical saturation was reached with no refusals or dropouts. Data were collected using a semi-structured interview guide developed from literature review and mixed methods guidelines. Thematic analysis was employed to analyze the data, enabling a comprehensive understanding of the participants’ perspectives. Results The thematic analysis of the interviews yielded three key themes. The first theme, ‘Understanding and Perceptions of Mixed Methods in Medical Education Research,’ delved into researchers’ depth of knowledge and conceptualization of mixed methods. The second theme, ‘Strategies and Integration in Mixed Methods Implementation,’ explored how these methodologies are applied and the challenges involved in their integration. The final theme, ‘Mastery in Mixed Methods: Prerequisites and Expert Consultation in Research,’ highlighted the gaps in readiness and expertise among researchers, emphasizing the importance of expert guidance in this field. Conclusion Findings indicate a varied understanding of mixed methods among participants. Some lacked a comprehensive grasp of its application, while others perceived mixed methods primarily as a means to enhance the publication prospects of their studies. There was a general lack of recognition of mixed methods as a guiding methodology for all study aspects, pointing to the need for more in-depth training and resources in this area.

Background This study developed, calibrated and evaluated a machine learning (ML) classifier designed to reduce study identification workload in maintaining the Cochrane COVID-19 Study Register (CCSR), a continuously updated register of COVID-19 research studies. Methods A ML classifier for retrieving COVID-19 research studies (the ‘Cochrane COVID-19 Study Classifier’) was developed using a data set of title-abstract records ‘included’ in, or ‘excluded’ from, the CCSR up to 18th October 2020, manually labelled by information and data curation specialists or the Cochrane Crowd. The classifier was then calibrated using a second data set of similar records ‘included’ in, or ‘excluded’ from, the CCSR between October 19 and December 2, 2020, aiming for 99% recall. Finally, the calibrated classifier was evaluated using a third data set of similar records ‘included’ in, or ‘excluded’ from, the CCSR between the 4th and 19th of January 2021. Results The Cochrane COVID-19 Study Classifier was trained using 59,513 records (20,878 of which were ‘included’ in the CCSR). A classification threshold was set using 16,123 calibration records (6005 of which were ‘included’ in the CCSR) and the classifier had a precision of 0.52 in this data set at the target threshold recall >0.99. The final, calibrated COVID-19 classifier correctly retrieved 2285 (98.9%) of 2310 eligible records but missed 25 (1%), with a precision of 0.638 and a net screening workload reduction of 24.1% (1113 records correctly excluded). Conclusions The Cochrane COVID-19 Study Classifier reduces manual screening workload for identifying COVID-19 research studies, with a very low and acceptable risk of missing eligible studies. It is now deployed in the live study identification workflow for the Cochrane COVID-19 Study Register.

Research on the business-environment dilemma has traditionally focused on strategies based on isolated, either/or mindsets, such as economically-oriented and environmentally-oriented strategies. Drawing on the cultural, philosophical, and intellectual traditions of China, we sketch the contours of a new holism-based strategic mindset, which results in a tian-ren-he-yi strategy. As an Eastern perspective, tian-ren-he-yi means “nature and mankind combined as one” or “nature-human harmony.” We leverage both qualitative and quantitative investigations to first identify the underlying mechanisms connecting tian-ren-he-yi strategy and firm performance, and then to compare the performance-enhancing potential of tian-ren-he-yi strategy with the two strategies based on the isolated mindset. Our analysis shows that when managing the business-environment dilemma, tian-ren-he-yi strategy has stronger performance-enhancing potential than either economically-oriented or environmentally-oriented strategies.

Migrant Capital covers a broad range of case studies and, by bringing together leading and emerging researchers, presents state-of-the-art empirical, theoretical and methodological perspectives on migration, networks, social and cultural capital, exploring the ways in which these bodies of literature can inform and strengthen each other.

The present volume is a broad overview of methods and methodologies in linguistics, illustrated with examples from concrete research. It collects insights gained from a broad range of linguistic sub-disciplines, ranging from core disciplines to topics in cross-linguistic and language-internal diversity or to contributions towards language, space and society. Given its critical and innovative nature, the volume is a valuable source for students and researchers of a broad range of linguistic interests.

Leading sociologists outline the historical development of the discipline in Britain and document its continuing influence in this essential and comprehensive reference work. Spanning the Scottish enlightenment of the 18th century to the present day this Handbook maps the discipline and the British contribution.

Modern science is, to a large extent, a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play here? These questions have kept philosophers of science busy for many years, and much work has been done to identify modeling as the central activity of theoretical science. At the same time, these questions have been addressed by methodologically-minded scientists, albeit from a different point of view. While philosophers typically have an eye on general aspects of scientific modeling, scientists typically take their own science as the starting point and are often more concerned with specific methodological problems. There is, however, also much common ground in middle, where philosophers and scientists can engage in a productive dialogue, as the present volume demonstrates. To do so, the editors of this volume have invited eight leading scientists from cosmology, climate science, social science, chemical engeneering and neuroscience to reflect upon their modeling work, and eight philosophers of science to provide a commentary.