Resident Development via Progress Testing and Test-Marking: An Innovation and Program Evaluation
Bibliographic record
Abstract
INTRODUCTION: Since 2008, the McMaster University Royal College Emergency Medicine residency training program has run practice Short Answer Question (SAQ) examinations to help residents test their knowledge and gain practice in answering exam-style questions. However, marking this type of SAQ exam is time-consuming. METHODS: To help address this problem, we require that senior residents help mark at least one exam per year alongside faculty members. Examinees' identities are kept anonymous by assigning a random number to each resident, which is only decoded after marking. Aggregation of marks is done by faculty only. The senior residents and faculty members all share sequential marking of each question. Each question is reviewed, and exemplar "best practice" answers are discussed. As novel/unusual answers appear, instantaneous fact-checking (via textbooks, or the internet) and discussions occur allowing for real-time modification to the answer keys as needed. RESULTS: A total of 22 out of 37 residents (post graduate year 1 to post graduate year 5 (PGY1 to PGY5)) participated in a recent program evaluation focus group. This evaluation showed that residents feel quite positive about this process. With the anonymization process, residents do not object to their colleagues seeing and marking their answers. Senior residents have found this process informative and have felt that this process helps them gain insight into better "examsmanship." CONCLUSIONS: Involving residents in marking short-answer exams is acceptable and perceived as useful experience for improving exam-taking skills. More studies of similar innovations would be required to determine to what extent this may be the case.
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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 | 0.217 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".