Anesthesiology Resident Induction Month: a pilot study showing an effective and safe way to train novice residents through simulation
Bibliographic record
Abstract
BACKGROUND: The transition of new residents from medical school to the post-graduate clinical environment remains challenging. We hypothesized that an introductory simulation course could improve new residents' performance in anesthesiology. METHODS: The Anesthesiology Residents Induction Month (ARIM) program was designed as a non-clinical simulation training program aiming at providing the theoretical and practical skills to safely approach, as junior anesthesiologists, the operating rooms. For each participant, specific knowledge, procedural skills and non-technical performance were assessed with a pre and post-test approach, before and immediately after the participation in the study. RESULTS: Fifteen first-month residents participated in the study. As compared to pre-test, residents significantly improved in all three evaluated areas. Pre-test knowledge assessment mean improved from 56% to 73% in the post-test (P<0.001). In the procedural skills assessment, pre-test mean improved from 43% to 77% (P<0.001) and non-technical skills assessment improved from 3.17 to 4.61 (in a scale out of seven points) in the post-test (P<0.001). CONCLUSIONS: Data suggest that an intensive simulation-based program can be an effective way for first-year residents to rapidly acquire and develop basic skills specific to anesthesiology. There might be benefits to begin residency with a training program aiming at developing and standardizing technical and non-technical skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".