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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A new adaptive testing algorithm for shortening health literacy assessments
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, August 2011
|
| DOI | 10.1186/1472-6947-11-52 |
| Pubmed ID | |
| Authors |
Sasikiran Kandula, Jessica S Ancker, David R Kaufman, Leanne M Currie, Qing Zeng-Treitler |
| Abstract |
Low health literacy has a detrimental effect on health outcomes, as well as ability to use online health resources. Good health literacy assessment tools must be brief to be adopted in practice; test development from the perspective of item-response theory requires pretesting on large participant populations. Our objective was to develop a novel classification method for developing brief assessment instruments that does not require pretesting on large numbers of research participants, and that would be suitable for computerized adaptive testing. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| United Kingdom | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 4% |
| Turkey | 1 | 2% |
| Brazil | 1 | 2% |
| Australia | 1 | 2% |
| Mexico | 1 | 2% |
| United Kingdom | 1 | 2% |
| Unknown | 46 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 19% |
| Student > Master | 10 | 19% |
| Professor > Associate Professor | 7 | 13% |
| Student > Doctoral Student | 5 | 9% |
| Professor | 3 | 6% |
| Other | 14 | 26% |
| Unknown | 4 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 17 | 32% |
| Social Sciences | 9 | 17% |
| Psychology | 5 | 9% |
| Computer Science | 3 | 6% |
| Nursing and Health Professions | 3 | 6% |
| Other | 7 | 13% |
| Unknown | 9 | 17% |
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 31 August 2011.
All research outputs
#15,233,109
of 22,649,029 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,305
of 1,977 outputs
Outputs of similar age
#85,001
of 120,112 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#10
of 10 outputs
Altmetric has tracked 22,649,029 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 1,977 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 24th percentile – i.e., 24% 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 120,112 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one.