The influence of the staffing variable relational nurse continuity on patient outcomes has been rarely studied and with inconclusive results. Multiple definitions and an absence of systematic methods for measuring the influence of continuity have resulted in its exclusion from nurse-staffing studies and conceptual models. We present a new conceptual model and an innovative use of health information technology to measure relational nurse continuity and to demonstrate the potential for bringing the results of big data science back to the bedside. Understanding the power of big data to address critical clinical issues may foster a new direction for nursing administration theory development.
This research output has an Altmetric Attention Score of 2. 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 29 March 2018.
All research outputs
#23,540,793
of 33,222,652 outputs
Outputs from Advances in Nursing Science
#489
of 705 outputs
Outputs of similar age
#191,542
of 296,667 outputs
Outputs of similar age from Advances in Nursing Science
#5
of 10 outputs
Altmetric has tracked 33,222,652 research outputs across all sources so far. This one is in the 29th percentile – i.e., 29% of other outputs scored the same or lower than it.
So far Altmetric has tracked 705 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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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. This one has scored higher than 5 of them.
