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Capturing judgement strategies in risk assessments with improved quality of clinical information: How nurses’ strategies differ from the ecological model

Overview of attention for article published in BMC Medical Informatics and Decision Making, January 2016
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Title
Capturing judgement strategies in risk assessments with improved quality of clinical information: How nurses’ strategies differ from the ecological model
Published in
BMC Medical Informatics and Decision Making, January 2016
DOI 10.1186/s12911-016-0243-1
Pubmed ID
Authors

Huiqin Yang, Carl Thompson

Abstract

Nurses' risk assessments of patients at risk of deterioration are sometimes suboptimal. Advances in clinical simulation mean higher quality information can be used as an alternative to traditional paper-based approaches as a means of improving judgement. This paper tests the hypothesis that nurses' judgement strategies and policies change as the quality of information used by nurses in simulation changes. Sixty-three student nurses and 34 experienced viewed 25 paper-case based and 25 clinically simulated scenarios, derived from real cases, and judged whether the (simulated) patient was at 'risk' of acute deterioration. Criteria of judgement "correctness" came from the same real cases. Information relative weights were calculated to examine judgement policies of individual nurses. Group comparisons of nurses and students under both paper and clinical simulation conditions were undertaken using non parametric statistical tests. Judgment policies were also compared to the ecological statistical model. Cumulative relative weights were calculated to assess how much information nurses used when making judgements. Receiver operating characteristic (ROC) curves were generated to examine predictive accuracy amongst the nurses. There were significant variations between nurses' judgement policies and those optimal policies determined by the ecological model. Nurses significantly underused the cues of consciousness level, respiration rate, and systolic blood pressure than the ecological model requires. However, in clinical simulations, they tended to make appropriate use of heart rate, with non-significant difference in the relative weights of heart rate between clinical simulations and the ecological model. Experienced nurses paid substantially more attention to respiration rate in the simulated setting compared to paper cases, while students maintained a similar attentive level to this cue. This led to a non-significant difference in relative weights of respiration rate between experienced nurses and students. Improving the quality of information by clinical simulations significantly impacted on nurses' judgement policies of risk assessments. Nurses' judgement strategies also varied with the increased years of experience. Such variations in processing clinical information may contribute to nurses' suboptimal judgements in clinical practice. Constructing predictive models of common judgement situations, and increasing nurses' awareness of information weightings in such models may help improve judgements made by nurses.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 68 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 16%
Student > Postgraduate 6 9%
Student > Bachelor 6 9%
Lecturer 4 6%
Other 4 6%
Other 18 26%
Unknown 19 28%
Readers by discipline Count As %
Nursing and Health Professions 28 41%
Medicine and Dentistry 8 12%
Psychology 3 4%
Social Sciences 2 3%
Pharmacology, Toxicology and Pharmaceutical Science 1 1%
Other 7 10%
Unknown 19 28%
Attention Score in Context

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 27 January 2016.
All research outputs
#20,258,310
of 25,765,370 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,629
of 2,158 outputs
Outputs of similar age
#284,662
of 406,670 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#31
of 35 outputs
Altmetric has tracked 25,765,370 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,158 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 21st percentile – i.e., 21% 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 406,670 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.