Rushing Roulette – how do learners perform routine tasks under time pressure?
Author and article information
Abstract
Introduction: Remediating, or preferably, predicting which residents will have difficulty before they need remediating, is a challenging task. Most of us perform better when pumped for an exam. But how do we respond when under routine pressures? Do weaker learners adapt differently, despite coaching? Methods: Using an adaptation of virtual patient software, we explored how learners cope with handling repetitive yet time-sensitive routine tasks. We emulated the performance of routine tasks within a virtual electronic medical record (EMR) environment, tracking individual learner activity and decision pathways, time to act (with and without enforced pressure from programmed time-outs) and their adaptation trajectories over time with coaching. Learners were assessed using Situational Judgement and modified Script Concordance Testing, with reproducible and granular time constraints introduced into the clinical reasoning process. Results: Our case designs introduce a number of competing elements: time pressures, competing priorities and instructions, resource availability and unpredictable outcomes. Learner behaviour is assessed using a variety of metrics including time-stamped decision points, decision pathways and internal counter scores. Clinical reasoning pathways, as compared to a reference peer panel, are in turn compared with and without the time pressures. Conclusions: Predictive analytics have made great promises in diagnosing problems for learners in difficulty but are complex and expensive to deploy widely. Our simpler, rapidly reproducible approach may provide a more practical solution.
Cite this as
2016. Rushing Roulette – how do learners perform routine tasks under time pressure? PeerJ Preprints 4:e1859v1 https://doi.org/10.7287/peerj.preprints.1859v1Author comment
Some final data analysis still pending around ROC data.
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Additional Information
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
David Topps conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Ana Popovic conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Teejay Horne analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Jean M Rawling performed the experiments, analyzed the data, wrote the paper, reviewed drafts of the paper.
Maureen Topps conceived and designed the experiments, performed the experiments, wrote the paper, reviewed drafts of the paper.
Human Ethics
The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):
Conjoint Health Research Ethics Board (CHREB), University of Calgary. Approval #: REB13-1028.
Data Deposition
The following information was supplied regarding data availability:
http://openlabyrinth.ca/signal-detection-theory-data-for-roulette-cases/
Funding
The authors received no funding for this work.
