Implementing a resident-led transition-specific bootcamp curriculum during pediatric residency training: Our 3-year experience
Bibliographic record
Abstract
Objectives: Postgraduate programs that use 'bootcamps' to help trainees transition into new clinical roles usually solely target entry to residency, failing to address other critical transition periods. We developed, implemented, and evaluated a resident-led transition-specific bootcamp curriculum in our Pediatrics program and described our first 3 years of experience. Methods: Our bootcamp curriculum was developed around Kern's framework. A needs assessment identified key transition periods (Incoming Resident [IR]; Night Float [NF]; Ward Senior [WS]). Teaching content and methods were informed by Residents-As-Teacher principles. Program evaluation included surveys exploring trainees' satisfaction, and perceived knowledge and self-efficacy before and after bootcamp participation. For the IR bootcamp, knowledge and behaviour were objectively assessed through written examinations and Observed Structured Clinical Examinations (OSCEs). Results: Twenty-seven pediatric residents participated in the IR bootcamp, 26 in the NF bootcamp, and 25 in the WS bootcamp. Trainees' baseline self-reported knowledge and confidence around clinical skills taught showed an improving trend post-bootcamp in all transition periods. Strengths identified included the level-appropriate teaching content and residents' engagement as teachers. Conclusions: Bootcamps can help residents adapt to new roles and should target all key transitions within the training continuum. Capitalizing on resident leadership for bootcamp curriculum development and implementation is instrumental to its success.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".