A practical approach to mentoring students with repeated performance deficiencies
Bibliographic record
Abstract
BACKGROUND: With the increasing use of competency-based evaluations we now have more and better ways to identify performance deficiencies in our learners. Yet the emphasis placed on identifying deficiencies appears to exceed that given to improving these deficiencies. AIMS: Here we describe the program at the University of Calgary for mentoring students with repeated performance deficiencies. We focus primarily on the key steps of mentoring and remediation, and establishing a program that provides consistency and accountability to this process. CONCLUSIONS: A small cohort of trainees with persistent performance deficiencies may need intensive remediation to reach the expected level of performance. Ultimately, not all learners will be successful in their remediation, but we feel that it is the responsibility of training programs to provide mentorship and an organized approach to remediation in order to maximize the chances of successful remediation.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".