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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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 50%
Professor 1 25%
Student > Bachelor 1 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 25%
Psychology 1 25%
Medicine and Dentistry 1 25%
Unknown 1 25%

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
#8,735,345
of 13,935,326 outputs
Outputs from BMC Research Notes
#1,587
of 3,178 outputs
Outputs of similar age
#187,492
of 360,204 outputs
Outputs of similar age from BMC Research Notes
#197
of 411 outputs
Altmetric has tracked 13,935,326 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,178 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
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We're also able to compare this research output to 411 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.