What's your best time? Chronometry in the learning of medical procedures
Bibliographic record
Abstract
OBJECTIVES: Most medical procedures have a time element. It is uncommon, however, to explicitly use chronometry, the measurement of time, in the learning of these procedures. This study considered whether instructional designs that include chronometry could improve deliberate practice and be used in meaningful formative assessments. METHODS: A selective review of the medical education literature was undertaken to identify how chronometry was used in a broad sampling of medical education research in the learning of medical procedures. We identified prior publications in which time measurement was used either directly as a pedagogic intervention or as an assessment method in a medical school programme. RESULTS: Our review suggests a number of desirable features of chronometry. For the individual learner, procedural time measurements can demonstrate both improving ability and increasing consistency. Chronometry can enhance instructional designs involving deliberate practice by facilitating overlearning (i.e. learning that goes beyond minimum competence), increasing the challenge level and enhancing self-regulation of learning (e.g. self-competition). Breaking down chronometric data into meaningful interval or split times might further inform instructional designs. CONCLUSIONS: Chronometry has the potential to contribute to instructional designs and assessment methods in medical procedures training. However, more research is needed to elucidate its full potential and describe possible negative consequences of this widely available but underutilised educational tool.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".