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Classification systems for causes of stillbirth and neonatal death, 2009–2014: an assessment of alignment with characteristics for an effective global system

Overview of attention for article published in BMC Pregnancy and Childbirth, September 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)

Mentioned by

twitter
11 tweeters
wikipedia
5 Wikipedia pages

Citations

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26 Dimensions

Readers on

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105 Mendeley
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Title
Classification systems for causes of stillbirth and neonatal death, 2009–2014: an assessment of alignment with characteristics for an effective global system
Published in
BMC Pregnancy and Childbirth, September 2016
DOI 10.1186/s12884-016-1040-7
Pubmed ID
Authors

Susannah Hopkins Leisher, Zheyi Teoh, Hanna Reinebrant, Emma Allanson, Hannah Blencowe, Jan Jaap Erwich, J. Frederik Frøen, Jason Gardosi, Sanne Gordijn, A. Metin Gülmezoglu, Alexander E. P. Heazell, Fleurisca Korteweg, Joy Lawn, Elizabeth M. McClure, Robert Pattinson, Gordon C. S. Smith, Ӧzge Tunçalp, Aleena M. Wojcieszek, Vicki Flenady

Abstract

To reduce the burden of 5.3 million stillbirths and neonatal deaths annually, an understanding of causes of deaths is critical. A systematic review identified 81 systems for classification of causes of stillbirth (SB) and neonatal death (NND) between 2009 and 2014. The large number of systems hampers efforts to understand and prevent these deaths. This study aimed to assess the alignment of current classification systems with expert-identified characteristics for a globally effective classification system. Eighty-one classification systems were assessed for alignment with 17 characteristics previously identified through expert consensus as necessary for an effective global system. Data were extracted independently by two authors. Systems were assessed against each characteristic and weighted and unweighted scores assigned to each. Subgroup analyses were undertaken by system use, setting, type of death included and type of characteristic. None of the 81 systems were aligned with more than 9 of the 17 characteristics; most (82 %) were aligned with four or fewer. On average, systems were aligned with 19 % of characteristics. The most aligned system (Frøen 2009-Codac) still had an unweighted score of only 9/17. Alignment with individual characteristics ranged from 0 to 49 %. Alignment was somewhat higher for widely used as compared to less used systems (22 % v 17 %), systems used only in high income countries as compared to only in low and middle income countries (20 % vs 16 %), and systems including both SB and NND (23 %) as compared to NND-only (15 %) and SB-only systems (13 %). Alignment was higher with characteristics assessing structure (23 %) than function (15 %). There is an unmet need for a system exhibiting all the characteristics of a globally effective system as defined by experts in the use of systems, as none of the 81 contemporary classification systems assessed was highly aligned with these characteristics. A particular concern in terms of global effectiveness is the lack of alignment with "ease of use" among all systems, including even the most-aligned. A system which meets the needs of users would have the potential to become the first truly globally effective classification system.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 105 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 24 23%
Student > Ph. D. Student 11 10%
Student > Postgraduate 9 9%
Researcher 8 8%
Student > Bachelor 7 7%
Other 12 11%
Unknown 34 32%
Readers by discipline Count As %
Medicine and Dentistry 47 45%
Nursing and Health Professions 11 10%
Psychology 4 4%
Social Sciences 3 3%
Agricultural and Biological Sciences 2 2%
Other 4 4%
Unknown 34 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 28 October 2021.
All research outputs
#2,532,577
of 20,983,497 outputs
Outputs from BMC Pregnancy and Childbirth
#689
of 3,786 outputs
Outputs of similar age
#46,123
of 290,208 outputs
Outputs of similar age from BMC Pregnancy and Childbirth
#1
of 1 outputs
Altmetric has tracked 20,983,497 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,786 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. This one has done well, scoring higher than 81% 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 290,208 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them