Teaching for the Transition: the Canadian PGY-1 Neurosurgery ‘Rookie Camp’
Bibliographic record
Abstract
BACKGROUND: Transitioning from medical school to residency is difficult and stressful, necessitating innovation in easing this transition. In response, a Canadian neurosurgical Rookie Camp was designed and implemented to foster acquisition of technical, cognitive and behavioral skills among incoming Canadian post graduate year one (PGY-1) neurosurgery residents. METHODS: The inaugural Rookie Camp was held in July 2012 in Halifax. The curriculum was developed based on a national needs-assessment and consisted of a pre-course manual, 7 case-based stations, 4 procedural skills stations and 2 group discussions. The content was clinically focused, used a variety of teaching methods, and addressed multiple CanMEDS competencies. Evaluation included participant and faculty surveys and a pre-course, post-course, and 3-month retention knowledge test. RESULTS: 17 of 23 PGY-1 Canadian neurosurgical residents participated in the Camp. All agreed the course content was relevant for PGY-1 training and the experience prepared them for residency. All participants would recommend the course to future neurosurgical residents. A statistically significant improvement was observed in knowledge related to course content (F(2,32) = 7.572, p<0.002). There were no significant differences between post-test and retention-test scores at three months. CONCLUSION: The inaugural Canadian Neurosurgery Rookie Camp for PGY-1 residents was successfully delivered, with engagement from participants, training programs, the Canadian Neurosurgical Society, and the Royal College. In addition to providing fundamental knowledge, which was shown to be retained, the course eased junior residents' transition to residency by fostering camaraderie and socialization within the specialty.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".