Evidence map›Paper›PMID 39102401›Full record

ArticlePloS one2024

Scientific misconduct responsibility attribution: An empirical study on byline position and team identity in Chinese medical papers.

Xiaoting Peng, Dehua Hu, Yi Guo, Hao Jiang, Xunsheng Wu, Qingyuan Hu

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xiaoting PengThe Third Xiangya Hospital of Central South University, ChangSha, Hunan, China.ORCID 0009-0007-9295-1880
Dehua HuDepartment of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, Hunan, China.
Yi GuoDepartment of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, Hunan, China.
Hao JiangDepartment of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, Hunan, China.
Xunsheng WuShenzhen Health Development Research and Data Management Center, Shenzhen, Guangdong, China.
Qingyuan HuThe Third Xiangya Hospital of Central South University, ChangSha, Hunan, China.ORCID 0000-0003-0838-6887

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe primary objective of this inquiry was to explore the nexus between authorship attribution in medical literature and accountability for scientific impropriety while assessing the influence of authorial multiplicity on the severity of sanctions imposed.

methodsProbit regression models were employed to scrutinize the impact of authorship on assuming accountability for scientific misconduct, and unordered multinomial logistic regression models were used to examine the influence of authorship and the number of bylines on the severity of punitive measures.

resultsFirst authors and corresponding authors were significantly more likely to be liable for scientific misconduct than other authors and were more likely to be penalized particularly severely. Furthermore, a negative correlation was observed between the number of authors' affiliations and the severity of punitive measures.

conclusionAuthorship exerts a pronounced influence on the attribution of accountability in scientific research misconduct, particularly evident in the heightened risk of severe penalties confronting first and corresponding authors owing to their principal roles. Hence, scientific research institutions and journals must delineate authorship specifications meticulously, ascertain authors' contributions judiciously, bolster initiatives aimed at fostering scientific research integrity, and uphold an environment conducive for robust scientific inquiry.

Indexed as

AuthorshipScientific MisconductChinaSocial Responsibility

Identifiers

PMID39102401
PMCPMC11299828

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.