The appeal process and beyond (part three of 3). When residents shouldn’t become clinicians: getting a grip on fair and defensible processes for termination of training
Bibliographic record
Abstract
Training programs have the dual responsibility of providing excellent training for their learners and ensuring their graduates are competent practitioners. Despite everyone's best efforts a small minority of learners will be unable to achieve competence and cannot graduate. Unfortunately, program decisions for training termination are often overturned, not because the academic decision was wrong, but because fair assessment processes were not implemented or followed. This series of three articles, intended for those setting residency program assessment policies and procedures, outlines recommendations, from establishing robust assessment foundations and the beginning of concerns (Part One), to established concerns and formal remediation (Part Two) to participating in formal appeals and after (Part Three). With these 14 recommendations on how to get a grip on fair and defensible processes for termination of training, career-impacting decisions that are both fair for the learner and defensible for programs are indeed possible. They are offered to minimize the chances of academic decisions being overturned, an outcome which wastes program resources, poses patient safety risks, and delays the resident finding a more appropriate career path. This article (Part Three in the series of three) will focus on the formal appeals and what to do after the appeal.
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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 | 0.030 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".