Situating Remediation: Accommodating Success and Failure in Medical Education Systems
Bibliographic record
Abstract
There has been a widespread shift to competency-based medical education (CBME) in the United States and Canada. Much of the CBME discourse has focused on the successful learner, with relatively little attention paid to what happens in CBME systems when learners stumble or fail. Emerging issues, such as the well-documented problem of "failure to fail" and concerns about litigious learners, have highlighted a need for well-defined and integrated frameworks to support and guide strategic approaches to the remediation of struggling medical learners.This Perspective sets out a conceptual review of current practices and an argument for a holistic approach to remediation in the context of their parent medical education systems. The authors propose parameters for integrating remediation into CBME and describe a model based on five zones of practice along with the rules of engagement associated with each zone. The zones are "normal" curriculum, corrective action, remediation, probation, and exclusion.The authors argue that, by linking and integrating theory and practice in remediation with CBME, a more integrated systems-level response to differing degrees of learner difficulty and failure can be developed. The proposed model demonstrates how educational practice in different zones is based on different rules, roles, responsibilities, and thresholds for moving between zones. A model such as this can help medical educators and medical education leaders take a more integrated approach to learners' failures as well as their successes by being more explicit about the rules of engagement that apply in different circumstances across the competency continuum.
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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 | 0.039 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.015 | 0.071 |
| Scholarly communication | 0.028 | 0.035 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".