Rushing Roulette – how do learners perform routine tasks under time pressure?
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".