Web-Based Learning for Emergency Airway Management in Anesthesia Residency Training
Bibliographic record
Abstract
Introduction. Web-based learning (WBL) is increasingly used in medical education; however, residency training programs often lack guidance on its implementation. We describe how the use of feasibility studies can guide the use of WBL in anesthesia residency training. Methods. Two case-based WBL emergency airway management modules were developed for self-directed use by anesthesia residents. The feasibility of using this educational modality was assessed using a single cohort pretest/posttest design. Outcome measures included user recruitment and retention rate, perceptions of educational value, and knowledge improvement. The differences between pre- and postmodule test scores and survey Likert scores were analysed using the paired t test. Results. Recruitment and retention rates were 90% and 65%, respectively. User-friendliness of the modules was rated highly. There was a significant improvement in perceptions of the value of WBL in the postsurvey. There was a significant knowledge improvement of 29% in the postmodule test. Conclusions. Feasibility studies can help guide appropriate use of WBL in curricula. While our study supported the potential feasibility of emergency airway management modules for training, collaboration with other anesthesia residency programs may enable more efficient development, implementation, and evaluation of this resource-intensive modality in anesthesia education and practice.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".