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Contrasting groups’ standard setting for consequences analysis in validity studies: reporting considerations

Overview of attention for article published in Advances in Simulation, March 2018
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Title
Contrasting groups’ standard setting for consequences analysis in validity studies: reporting considerations
Published in
Advances in Simulation, March 2018
DOI 10.1186/s41077-018-0064-7
Pubmed ID
Authors

Morten Jørgensen, Lars Konge, Yousif Subhi

Abstract

The contrasting groups' standard setting method is commonly used for consequences analysis in validity studies for performance in medicine and surgery. The method identifies a pass/fail cut-off score, from which it is possible to determine false positives and false negatives based on observed numbers in each group. Since groups in validity studies are often small, e.g., due to a limited number of experts, these analyses are sensitive to outliers on the normal distribution curve. We propose that these shortcomings can be addressed in a simple manner using the cumulative distribution function. We demonstrate considerable absolute differences between the observed false positives/negatives and the theoretical false positives/negatives. In addition, several important examples are given. We propose that a better reporting strategy is to report theoretical false positives and false negatives together with the observed false positives and negatives, and we have developed an Excel sheet to facilitate such calculations. Not relevant.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 19%
Student > Ph. D. Student 8 17%
Researcher 5 10%
Student > Bachelor 5 10%
Other 4 8%
Other 7 15%
Unknown 10 21%
Readers by discipline Count As %
Medicine and Dentistry 23 48%
Biochemistry, Genetics and Molecular Biology 3 6%
Social Sciences 2 4%
Nursing and Health Professions 2 4%
Unspecified 1 2%
Other 2 4%
Unknown 15 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 18 March 2018.
All research outputs
#13,346,498
of 23,026,672 outputs
Outputs from Advances in Simulation
#214
of 235 outputs
Outputs of similar age
#167,441
of 332,332 outputs
Outputs of similar age from Advances in Simulation
#5
of 5 outputs
Altmetric has tracked 23,026,672 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 235 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.8. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
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 332,332 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.