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Improving Hospital Patient Falls

Overview of attention for article published in Journal of Nursing Administration, May 2015
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

twitter
4 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
34 Mendeley
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Title
Improving Hospital Patient Falls
Published in
Journal of Nursing Administration, May 2015
DOI 10.1097/nna.0000000000000195
Pubmed ID
Authors

Carolyn Aydin, Nancy Donaldson, Harriet Udin Aronow, Moshe Fridman, Diane Storer Brown

Abstract

Predictive models for falls, injury falls, and restraint prevalence were explored within nursing unit structures and processes of care. The patient care team is responsible for patient safety, and improving practice models may prevent injuries and improve patient safety. Using unit-level self-reported data from 215 hospitals, falls, injury falls, and restraint prevalence were modeled with significant covariates as predictors. Fewer falls/injury falls were predicted by populations with fewer frail and at-risk patients, more unlicensed care hours, and prevention protocol implementation, but not staffing per se, restraint use, or RN expertise. Lower restraint use was predicted by fewer frail patients, shorter length of stay, more RN hours, more certified RNs, and implementation of fall prevention protocols. In the presence of risk, patient injuries and safety were improved by optimizing staffing skill mix and use of prevention protocols.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 21%
Student > Bachelor 6 18%
Student > Ph. D. Student 5 15%
Student > Master 5 15%
Professor 3 9%
Other 6 18%
Unknown 2 6%
Readers by discipline Count As %
Nursing and Health Professions 17 50%
Medicine and Dentistry 7 21%
Social Sciences 2 6%
Arts and Humanities 1 3%
Linguistics 1 3%
Other 3 9%
Unknown 3 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 07 February 2016.
All research outputs
#7,957,106
of 15,350,461 outputs
Outputs from Journal of Nursing Administration
#602
of 1,446 outputs
Outputs of similar age
#87,199
of 231,548 outputs
Outputs of similar age from Journal of Nursing Administration
#3
of 33 outputs
Altmetric has tracked 15,350,461 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,446 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 58% 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 231,548 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.