Piloting a Basic Life Support instructor course: A short report
Bibliographic record
Abstract
Aim: The aim was to describe a new shortened pilot of the European Resuscitation Council's standard Basic Instructor Course. Methods: The four-hour pilot followed a blended learning strategy (pre-course preparation, on-site small-group sessions). Each participant taught a short Basic Life Support competency to the group (micro-teaching) and received the group's feedback. A feedback "drill" session followed. Primary quantitative outcome was the proportion of Basic Instructor Course participants subsequently teaching Basic Life Support. Post-course teachings were recorded and compared to standard eight-hour Basic Instructor Courses. Participants' open feedback question answers were qualitatively analyzed and presented descriptively. Results: This pilot Basic Instructor Course taught 31 healthcare providers in 4 courses in 2019-2021 (aged 31.5 ± 12.9 years; 61 % women; 29 % physicians; 71 % medical students; 21 % no teaching experience). Participants reported that they gained most from micro-teaching (64 %), and advice on their teaching (50 %). Some judged the course as being too long (29 %). Twenty-seven pilot course participants (87 %) (including three instructor candidates) started teaching, whereas only nine of 37 participants of the 3 courses (24 %, including three instructor candidates) from the standard eight-hour course did. Conclusion: Participants of the pilot shortened Basic Instructor Course in a healthcare setting were successfully trained to teach European Resuscitation Council's Basic Life Support provider courses in a short four-hour format. The pilot course seems to enable future instructors to teach Basic Life Support provider courses. Higher motivation to teach resulted in four times as many instructors who taught courses after the pilot course compared to the standard 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".