Time-Variable Training in Medicine: Theoretical Considerations
Bibliographic record
Abstract
The introduction of competency-based medical education has shifted thinking from a fixed-time model to one stressing attained competencies, independent of the time needed to arrive at those competencies. In this article, the authors explore theoretical and conceptual issues related to time variability in medical training, starting with the Carroll model from the 1960s that put time in the equation of learning. They discuss mastery learning, deliberate practice, and learning curves.While such behaviorist theories apply well to structured courses and highly structured training settings, learning in the clinical workplace is not well captured in such theories or in the model that Carroll proposed. Important in clinical training are self-regulation and motivation; neurocognitive perspectives of time and learning; professional identity formation; and entrustment as an objective of training-all of which may be viewed from the perspective of the time needed to complete training. The authors conclude that, in approaching time variability, the Carroll equation is too simplistic in its application to the breadth of medical training. The equation may be expanded to include variables that determine effective workplace learning, but future work will need to examine the validity of these additional factors.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".