Current situation and influence factors of scientific integrity in China: A multicenter survey

Objective

Cases of scientific misconduct have occurred frequently, especially in the field of medical research. We collected electronic questionnaires from 1257 medical staff in 43 cities and obtained a cross-sectional data set of their understanding of scientific integrity in research. This study aims to propose recommendations for establishing a mature oversight system for research integrity.

Methods

The study employed multiple regression analysis to explore the effect of different factors on the perception of four types of research integrity.

Results

Female participants had a higher understanding of project application integrity than men (P < ​ 0.001). Participants in clinical departments had a lower understanding of project application integrity than those in nursing departments (clinical vs. nursing, P ​= 0.046). Participants with a junior college degree or below had a lower understanding than those who had a postgraduate degree and doctoral degree (junior college or below vs. postgraduate degree, P ​< ​0.001; junior college or below vs. doctoral degree, P ​< ​0.001).

Conclusions

We found that female, medical technology department, advanced education background, and advanced professional titles were significantly associated with a higher understanding of scientific integrity in research in China.

[Establishment of the new "Health Data Lab" to provide data for science]

The analysis of real-world data (RWD) has become increasingly important in health research in recent years. With the BfArM Health Data Lab (HDL), which is currently being set up, researchers will in future be able to gain access to routine data from the statutory health insurance of around 74 million people in Germany. Data from electronic patient records can also be made available for research prospectively. In doing so, the Health Data Lab guarantees the highest data protection and IT security standards. The digital application process, the provision of data in secure processing environments as well as the features supporting the analyses such as catalogues of coding systems, a point-and-click analysis tool and predefined standard analyses increase user-friendliness for researchers. The use of the extensive health data accessible at HDL will open a wide range of future possibilities for improving the health system and the quality of care. This article begins by highlighting the advantages of the HDL and outlining the opportunities that the RWD offers for research in healthcare and for the population. The structure and central aspects of the HDL are explained afterwards. An outlook on the opportunities of linking different data is given. What the application and data usage processes at the HDL will look like is illustrated using the example of fictitious possibilities for analysing long COVID based on the routine data available at the HDL in the future.