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Rapid sociometric mapping of community health workers to identify opinion leaders using an SMS platform: a short report

Overview of attention for article published in Implementation Science, June 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
24 tweeters
facebook
1 Facebook page

Citations

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4 Dimensions

Readers on

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101 Mendeley
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Title
Rapid sociometric mapping of community health workers to identify opinion leaders using an SMS platform: a short report
Published in
Implementation Science, June 2017
DOI 10.1186/s13012-017-0611-y
Pubmed ID
Authors

Thomas A. Odeny, Maya Petersen, Charles T. Muga, Jayne Lewis-Kulzer, Elizabeth A. Bukusi, Elvin H. Geng

Abstract

Using opinion leaders to accelerate the dissemination of evidence-based public health practices is a promising strategy for closing the gap between evidence and practice. Network interventions (using social network data to accelerate behavior change or improve organizational performance) are a promising but under-explored strategy. We aimed to use mobile phone technology to rapidly and inexpensively map a social network and identify opinion leaders among community health workers in a large HIV program in western Kenya. We administered a five-item socio-metric survey to community health workers using a mobile phone short message service (SMS)-based questionnaire. We used the survey results to construct and characterize a social network of opinion leaders among respondents. We calculated the extent to which a particular respondent was a popular point of reference ("degree centrality") and the influence of a respondent within the network ("eigenvector centrality"). Surveys were returned by 38/39 (97%) of peer health workers contacted; 52% were female. The median survey response time was 13.75 min (inter-quartile range, 8.8-38.7). The total cost of relaying survey questions through a secure cloud-based SMS aggregator was $8.46. The most connected individuals (high degree centrality) were also the most influential (high eigenvector centrality). The distribution of influence (eigenvector centrality) was highly skewed in favor of a single influential individual at each site. Leveraging increasing access to SMS technology, we mapped the network of influence among community health workers associated with a HIV treatment program in Kenya. Survey uptake was high, response rates were rapid, and the survey identified clear opinion leaders. In sum, we offer proof of concept that a "mobile health" (mHealth) approach can be used in resource-limited settings to efficiently map opinion leadership among health care workers and thus open the door to reproducible, feasible, and efficient empirically based network interventions that seek to spread novel practices and behaviors among health care workers.

Twitter Demographics

The data shown below were collected from the profiles of 24 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 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 101 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 19%
Student > Master 14 14%
Researcher 12 12%
Student > Bachelor 8 8%
Student > Doctoral Student 5 5%
Other 21 21%
Unknown 22 22%
Readers by discipline Count As %
Medicine and Dentistry 19 19%
Social Sciences 13 13%
Nursing and Health Professions 12 12%
Psychology 7 7%
Computer Science 6 6%
Other 18 18%
Unknown 26 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 20 August 2017.
All research outputs
#1,850,418
of 17,778,103 outputs
Outputs from Implementation Science
#506
of 1,591 outputs
Outputs of similar age
#42,163
of 276,004 outputs
Outputs of similar age from Implementation Science
#5
of 7 outputs
Altmetric has tracked 17,778,103 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,591 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.1. This one has gotten more attention than average, scoring higher than 68% 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 276,004 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 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.