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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Using latent class analysis to model prescription medications in the measurement of falling among a community elderly population
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, May 2013
|
| DOI | 10.1186/1472-6947-13-60 |
| Pubmed ID | |
| Authors |
Patrick C Hardigan, David C Schwartz, William D Hardigan |
| Abstract |
Falls among the elderly are a major public health concern. Therefore, the possibility of a modeling technique which could better estimate fall probability is both timely and needed. Using biomedical, pharmacological and demographic variables as predictors, latent class analysis (LCA) is demonstrated as a tool for the prediction of falls among community dwelling elderly. |
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 % |
|---|---|---|
| India | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 2 | 5% |
| United States | 2 | 5% |
| Unknown | 39 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 21% |
| Student > Bachelor | 9 | 21% |
| Student > Ph. D. Student | 7 | 16% |
| Student > Master | 5 | 12% |
| Student > Doctoral Student | 3 | 7% |
| Other | 7 | 16% |
| Unknown | 3 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 26% |
| Nursing and Health Professions | 6 | 14% |
| Social Sciences | 4 | 9% |
| Sports and Recreations | 4 | 9% |
| Psychology | 2 | 5% |
| Other | 10 | 23% |
| Unknown | 6 | 14% |
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 30 August 2013.
All research outputs
#15,272,611
of 22,711,242 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,307
of 1,981 outputs
Outputs of similar age
#120,121
of 195,058 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#23
of 27 outputs
Altmetric has tracked 22,711,242 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 1,981 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 195,058 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.