To fail is human: remediating remediation in medical education
Bibliographic record
Abstract
INTRODUCTION: Remediating failing medical learners has traditionally been a craft activity responding to individual learner and remediator circumstances. Although there have been moves towards more systematic approaches to remediation (at least at the institutional level), these changes have tended to focus on due process and defensibility rather than on educational principles. As remediation practice evolves, there is a growing need for common theoretical and systems-based perspectives to guide this work. METHODS: This paper steps back from the practicalities of remediation practice to take a critical systems perspective on remediation in contemporary medical education. In doing so, the authors acknowledge the complex interactions between institutional, professional, and societal forces that are both facilitators of and barriers to effective remediation practices. RESULTS: The authors propose a model that situates remediation within the contexts of society as a whole, the medical profession, and medical education institutions. They also outline a number of recommendations to constructively align remediation principles and practices, support a continuum of remediation practices, destigmatize remediation, and develop institutional communities of practice in remediation. DISCUSSION: Medical educators must embrace a responsible and accountable systems-level approach to remediation if they are to meet their obligations to provide a safe and effective physician workforce.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".