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Simple decision-tree tool to facilitate author identification of reporting guidelines during submission: a before–after study

Overview of attention for article published in Research Integrity and Peer Review, December 2017
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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63 tweeters

Citations

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5 Dimensions

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25 Mendeley
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Title
Simple decision-tree tool to facilitate author identification of reporting guidelines during submission: a before–after study
Published in
Research Integrity and Peer Review, December 2017
DOI 10.1186/s41073-017-0044-9
Pubmed ID
Authors

Daniel R. Shanahan, Ines Lopes de Sousa, Diana M. Marshall

Abstract

There is evidence that direct journal endorsement of reporting guidelines can lead to important improvements in the quality and reliability of the published research. However, over the last 20 years, there has been a proliferation of reporting guidelines for different study designs, making it impractical for a journal to explicitly endorse them all. The objective of this study was to investigate whether a decision tree tool made available during the submission process facilitates author identification of the relevant reporting guideline. This was a prospective 14-week before-after study across four speciality medical research journals. During the submission process, authors were prompted to follow the relevant reporting guideline from the EQUATOR Network and asked to confirm that they followed the guideline ('before'). After 7 weeks, this prompt was updated to include a direct link to the decision-tree tool and an additional prompt for those authors who stated that 'no guidelines were applicable' ('after'). For each article submitted, the authors' response, what guideline they followed (if any) and what reporting guideline they should have followed (including none relevant) were recorded. Overall, 590 manuscripts were included in this analysis-300 in the before cohort and 290 in the after. There were relevant reporting guidelines for 75% of manuscripts in each group; STROBE was the most commonly applicable reporting guideline, relevant for 35% (n = 106) and 37% (n = 106) of manuscripts, respectively. Use of the tool was associated with an 8.4% improvement in the number of authors correctly identifying the relevant reporting guideline for their study (p < 0.0001), a 14% reduction in the number of authors incorrectly stating that there were no relevant reporting guidelines (p < 0.0001), and a 1.7% reduction in authors choosing a guideline (p = 0.10). However, the 'after' cohort also saw a significant increase in the number of authors stating that there were relevant reporting guidelines for their study, but not specifying which (34 vs 29%;p = 0.04). This study suggests that use of a decision-tree tool during submission of a manuscript is associated with improved author identification of the relevant reporting guidelines for their study type; however, the majority of authors still failed to correctly identify the relevant guidelines.

Twitter Demographics

The data shown below were collected from the profiles of 63 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 28%
Researcher 4 16%
Student > Bachelor 4 16%
Other 3 12%
Student > Ph. D. Student 2 8%
Other 3 12%
Unknown 2 8%
Readers by discipline Count As %
Medicine and Dentistry 9 36%
Biochemistry, Genetics and Molecular Biology 3 12%
Nursing and Health Professions 3 12%
Psychology 2 8%
Physics and Astronomy 1 4%
Other 2 8%
Unknown 5 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 34. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 February 2019.
All research outputs
#562,368
of 14,535,614 outputs
Outputs from Research Integrity and Peer Review
#33
of 82 outputs
Outputs of similar age
#24,463
of 400,505 outputs
Outputs of similar age from Research Integrity and Peer Review
#8
of 14 outputs
Altmetric has tracked 14,535,614 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 82 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 37.4. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 400,505 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.