Progress testing in family medicine – A novel use for simulated office oral exams
Bibliographic record
Abstract
BACKGROUND: Simulated office orals (SOOs) are used by the College of Family Physicians of Canada as a method to evaluate family medicine resident readiness for clinical practice. The use of SOOs as a progress test would provide residency programs with useful information to determine resident readiness for challenging the certification exam. The data from a progress test could then be easily manipulated to generate a risk assessment plot. METHODS: During a prospective cohort study conducted at the University of Ottawa, the feasibility of using practice SOO sessions, a structured clinical exam, as a progress test was explored. Twenty-three residents participated in all four practice SOO sessions and their results were entered into a risk assessment plot. RESULTS: Repeated measures analysis of the data using ANOVA demonstrated that the residents' scores at each time point were statistically different from each other, generating an F(3, 66) = 27.52, p < 0.001, η(2) = 0.55.and that the relationship over time was linear with an F(1, 22) = 123.80, p < 0.001, η(2) = 0.85. At the final time point, a risk assessment resulted in no learners mapping to quadrants III or IV. CONCLUSIONS: Our results demonstrate the feasibility of utilizing a SOO exam, a clinical exam, as a progress test. In addition, we propose generating a risk assessment plot, using the data from the Fall 2013 and Spring 2014 practice SOO sessions, as a means of identifying residents at risk. Further studies will be needed to confirm the utility of this analysis. Combined with other measures acquired during in-training evaluation, the utilization of practice SOOs as a progress test will provide program directors with valuable information on resident progression.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".