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Improving Learner Handovers in Medical Education

Overview of attention for article published in Academic medicine, July 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

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43 X users
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1 Facebook page

Citations

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37 Dimensions

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86 Mendeley
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Title
Improving Learner Handovers in Medical Education
Published in
Academic medicine, July 2017
DOI 10.1097/acm.0000000000001457
Pubmed ID
Authors

Eric J. Warm, Robert Englander, Anne Pereira, Paul Barach

Abstract

Multiple studies have demonstrated that the information included in the Medical Student Performance Evaluation fails to reliably predict medical students' future performance. This faulty transfer of information can lead to harm when poorly prepared students fail out of residency or, worse, are shuttled through the medical education system without an honest accounting of their performance. Such poor learner handovers likely arise from two root causes: (1) the absence of agreed-on outcomes of training and/or accepted assessments of those outcomes, and (2) the lack of standardized ways to communicate the results of those assessments. To improve the current learner handover situation, an authentic, shared mental model of competency is needed; high-quality tools to assess that competency must be developed and tested; and transparent, reliable, and safe ways to communicate this information must be created.To achieve these goals, the authors propose using a learner handover process modeled after a patient handover process. The CLASS model includes a description of the learner's Competency attainment, a summary of the Learner's performance, an Action list and statement of Situational awareness, and Synthesis by the receiving program. This model also includes coaching oriented towards improvement along the continuum of education and care. Just as studies have evaluated patient handover models using metrics that matter most to patients, studies must evaluate this learner handover model using metrics that matter most to providers, patients, and learners.

X Demographics

X Demographics

The data shown below were collected from the profiles of 43 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 86 100%

Demographic breakdown

Readers by professional status Count As %
Other 14 16%
Professor > Associate Professor 9 10%
Student > Master 9 10%
Researcher 7 8%
Student > Doctoral Student 6 7%
Other 27 31%
Unknown 14 16%
Readers by discipline Count As %
Medicine and Dentistry 47 55%
Social Sciences 5 6%
Nursing and Health Professions 3 3%
Business, Management and Accounting 2 2%
Psychology 2 2%
Other 9 10%
Unknown 18 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 20 October 2021.
All research outputs
#1,571,617
of 25,371,288 outputs
Outputs from Academic medicine
#767
of 6,818 outputs
Outputs of similar age
#30,370
of 326,824 outputs
Outputs of similar age from Academic medicine
#16
of 123 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,818 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.8. This one has done well, scoring higher than 88% 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 326,824 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 123 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.