The impact of intense airway management training on paramedic knowledge and confidence measured before, immediately after and at 6 and 12 months after training
Bibliographic record
Abstract
INTRODUCTION: Airway management is a core component in the practice of advanced life support (ALS) paramedics. OBJECTIVE: To determine if an intense airway management course would improve ALS paramedic knowledge and confidence and if knowledge was retained over time. METHODS: An identical written survey (measuring demographics and confidence) and multiple-choice examination (measuring knowledge) was administered at the start and end of a 10 h airway course. At 6 and 12 months after the course, paramedics took the knowledge examination. Paired confidence and written knowledge examination scores before and immediately after the course were compared. Differences between knowledge examination scores at all four time points (before, immediately after and at 6 and 12 months) were tested using analysis of variance and Tukey's test. RESULTS: 299 ALS paramedics were enrolled in the study. 209 (69%) reported 6 or more years of ALS experience. The mean pre-course confidence score was 2.74/4 and the mean post-course confidence score was 3.39/4; a difference of 0.7 points (95% CI 0.61 to 0.71). Post-course examination scores improved by 4.9 points (95% CI 4.58 to 5.20), from 7.7 to 12.6/20. At 6 months the mean score was 10.3/20, and at 12 months 10.2/20. Post-course scores were significantly better than pre-course scores. Scores at 6 and 12 months did not differ significantly and remained significantly improved from pre-course scores. CONCLUSION: Significant improvement in confidence and knowledge was found after paramedics completed an intense airway management course. Knowledge at 6 and 12 months remained significantly better compared with pre-course.
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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 | 0.010 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".