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Does the presence of siblings affect the results produced by a surveillance system of child mistreatment? Comparisons of several commonly-used statistical methods

Overview of attention for article published in BMC Research Notes, December 2015
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Title
Does the presence of siblings affect the results produced by a surveillance system of child mistreatment? Comparisons of several commonly-used statistical methods
Published in
BMC Research Notes, December 2015
DOI 10.1186/s13104-015-1710-y
Pubmed ID
Authors

Christelle Senterre, Alain Levêque, Brigitte Vanthournout, Michèle Dramaix

Abstract

Over time, the circumstances encountered in case of child mistreatment, can be quite complex and then, can lead to methodological questions for the analysis of the data. Based on data coming from 395 children hospitalized, alone (66.1 %) or in siblings (33.9 %), in a pediatric ward between 2007 and 2012 for mistreatment or because of a severe risk of mistreatment, the aims of this paper were to quantify the degree of similarity between sibling members, to study the differences between children hospitalized alone or with siblings and to compare four statistical methods (logistic regression and GEE, both without and with robust standard error) for the analyses of the associated factors of mistreatment. Almost all intracluster correlation coefficients were large, meaning that the sibling's members have a higher degree of similarity between them. The odds ratios were not exactly the same between the two models and the robust standard errors where almost always higher than the model-based standard errors in both logistic and GEE models leading to wider confidence intervals. Because many of the intra-siblings correlations observed were relatively strong, the failure to take this cluster dependency into account had a substantial effect on the statistical analyses. Methods taking into account the cluster dependency are widely available in statistical software and strongly recommended.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 29%
Professor 1 14%
Student > Bachelor 1 14%
Researcher 1 14%
Unknown 2 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 14%
Psychology 1 14%
Social Sciences 1 14%
Medicine and Dentistry 1 14%
Unknown 3 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 09 December 2015.
All research outputs
#15,351,847
of 22,835,198 outputs
Outputs from BMC Research Notes
#2,315
of 4,265 outputs
Outputs of similar age
#228,240
of 389,038 outputs
Outputs of similar age from BMC Research Notes
#77
of 146 outputs
Altmetric has tracked 22,835,198 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,265 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.