Preparing Psychiatry Residents for the Certification Exam: A Survey of Residency and Exam Experiences
Bibliographic record
Abstract
OBJECTIVE: To determine which methods best prepare psychiatry residents for the certification exam, and ultimately for practice, to facilitate appropriate residency program curriculum changes. METHOD: We sent an anonymous survey to all final year (that is, PGY5) Canadian university-affiliated psychiatry residents, regarding frequency and diversity of observed interviews, form of feedback delivery, research and other training experiences, self-perception of preparedness and knowledge base, and management strategies for exam anxiety 6 months before and immediately after the certification exam. RESULTS: There was a 52% response rate. Residents from across Canada identified the following factors as enabling successful exam completion: regular mock orals supervised by Royal College examiners, clinical experience with exposure to a wide spectrum of pathologies, individual and group study time, and appropriate anxiety management. Preparation for the oral exam involving sample case vignettes with presentation and formulation skills training was considered to be essential but was identified as an area of educational and experiential weakness in some programs. CONCLUSIONS: To prepare psychiatry residents for successful completion of their certification exam, programs should incorporate regular mock orals observed by Royal College examiners throughout residency training (not just in PGY2 and PGY5). Programs should also incorporate training in case vignettes, training in oral exam skills, and teaching of anxiety-management strategies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".