A Newly Developed Interprofessional <i>In-Situ</i> Simulation-Based Training for Airway Management of COVID-19 Patients: Identification of Challenges and Safety Gaps, and Assessment of the Participants’ Reaction
Bibliographic record
Abstract
Background: simulation (ISS) airway management training in confirmed or suspected COVID-19 patients for emergency and anesthesiology staff, evaluated participants' reactions, and identified perceived challenges. Methods: We used a cross-sectional study design incorporating a quantitative questionnaire to describe participants' reaction to the ISS and a qualitative group interview using the plus-delta debriefing modality to explore participants' challenges in acquiring the knowledge and skills required for each learning objective. Data were analyzed using descriptive statistics and deductive content analysis. Results: Two hundred and ninety-nine healthcare providers participated in 62 ISS training sessions. Over 90% of our study participants agreed or strongly agreed that: they understood the learning objectives; the training material appropriately challenged them; the course content was relevant, easy to navigate, and essential; the facilitators' knowledge, teaching, and style were appropriate; the simulation facilities were suitable; and they had ample opportunities to practice the learned skills. The main challenges identified were anticipating difficult airways, preparing intubation equipment, minimizing the number of personnel inside the room, adhering to the proper doffing sequence, preparing needed equipment outside the intubation room, speaking up, and ensuring closed-loop communication. Conclusion: The newly developed ISS training was feasible for busy healthcare practitioners to safely perform airway management procedures for suspected or confirmed COVID-19 patients without affecting bedside care. Anticipation of difficult airways and speaking up were the most frequent challenges identified across all specialties in this study.
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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 | 0.003 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".