Effects of Postgraduate Medical Education “Boot Camps” on Clinical Skills, Knowledge, and Confidence: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Throughout their medical education, learners face multiple transition periods associated with increased demands, producing stress and concern about the adequacy of their skills for their new role. OBJECTIVE: We evaluated the effectiveness of boot camps in improving clinical skills, knowledge, and confidence during transitions into postgraduate or discipline-specific residency programs. METHODS: Boot camps are in-training courses combining simulation-based practice with other educational methods to enhance learning and preparation for individuals entering new clinical roles. We performed a search of MEDLINE, CINAHL, PsycINFO, EMBASE, and ERIC using boot camp and comparable search terms. Inclusion criteria included studies that reported on medical education boot camps, involved learners entering new clinical roles in North American programs, and reported empirical data on the effectiveness of boot camps to improve clinical skills, knowledge, and/or confidence. A random effects model meta-analysis was performed to combined mean effect size differences (Cohen's d) across studies based on pretest/posttest or comparison group analyses. RESULTS: The search returned 1096 articles, 15 of which met all inclusion criteria. Combined effect size estimates showed learners who completed boot camp courses had significantly "large" improvements in clinical skills (d = 1.78; 95% CI 1.33-2.22; P < .001), knowledge (d = 2.08; 95% CI 1.20-2.96; P < .001), and confidence (d = 1.89; 95% CI 1.63-2.15; P < .001). CONCLUSIONS: Boot camps were shown as an effective educational strategy to improve learners' clinical skills, knowledge, and confidence. Focus on pretest/posttest research designs limits the strength of these findings.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.045 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".