Practice simulated office orals as a predictor of Certification examination performance in family medicine.
Bibliographic record
Abstract
OBJECTIVE: To determine if performance on practice simulated office orals (SOOs) conducted during residency training could predict residents' performance on the SOO component of the College of Family Physicians of Canada's (CFPC's) final Certification examination. DESIGN: Prospective cohort study. SETTING: University of Ottawa in Ontario. PARTICIPANTS: Family medicine residents enrolled in the University of Ottawa's Family Medicine Residency program between July 1, 2012, and June 30, 2014, who were eligible to write the CFPC Certification examination in the spring of 2014 and who had participated in all 4 practice SOO examination sessions; 23 residents met these criteria. MAIN OUTCOME MEASURES: Scores on practice SOO sessions during fall 2012, spring 2013, fall 2013, and spring 2014; and the SOO component score on the spring 2014 administration of the CFPC Certification examination. RESULTS: < .001) and that the relationship over time could be represented by either a linear relationship or a quadratic relationship. A generalizability study generated a relative generalizability coefficient of 0.63. CONCLUSION: Our results confirm the usefulness of practice SOOs as a progress test and demonstrate the feasibility of using them to predict final scores on the SOO component of the CFPC's Certification examination.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".