The Junior Resident's Perspective of Learning in a Simulation‐Based Otolaryngology Boot Camp
Bibliographic record
Abstract
Objectives: 1) Determine what residents experience in an otolaryngology boot camp (OBC). 2) Understand how the individual resident's background influences learning. Methods: Using a qualitative phenomenological approach, investigators interviewed 36 junior otolaryngology residents who had participated in a one‐day simulation‐based OBC. The residents attended camps in Washington, DC (July 2012), or in London, Ontario (September 2012). A semi‐structured interview of each resident was recorded and transcribed. Using Moustakaa'ss analysis, the interviews were broken up into codes and clusters to create a codebook. To ensure trustworthiness of the qualitative data, investigators used upfront debriefing, investigator triangulation, epoche, reciprocal coding of transcripts, member checks, and thick, rich description. Results: Five learning themes emerged during OBC: 1) Residents aim to gain knowledge and experience to positively affect their performance and patient outcomes. 2) Prior clinical experience and OBC's realistic scenarios influenced resident learning. 3) The residents valued their development of leadership and teamwork. 4) Residents actively learn by synthesis and application of their knowledge. 5) The faculty plays a critical role in the boot camp. Conclusions: The Accreditation Council for Graduate Medical Education (ACGME) has specific core requirements to improve resident performance and competency. To address this, innovative otolaryngology faculty has developed specialty specific boot camps, and quantitative research has documented the learners’ performance gains. Using the residents’ own words, this qualitative research study shows how residents learn and why residents benefit from attending a simulation‐based otolaryngology boot camp. This research will help us better understand our learners and how we can improve their learning experience.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".