How to design a blended method to teach BLS-AED for undergraduate nursing and medical education
Bibliographic record
Abstract
New technology can help to spread knowledge and skills related to cardiopulmonary resuscitation. Historically, virtual methods have not been strongly recommended for Consell Català de Resuscitació (CCR), but many authors have begun to investigate new methods for achieving lower costs, widespread distribution, increased accessibility to information and more frequent updates of content. Moodle platforms, videos, web pages and other technologies have been introduced in the learning world. The authors posit that a blended approach of traditional methods with virtual methods without moving away from standard recommendations could facilitate the introduction of these new methods into our teaching practice. In this article, the authors present how to design a blended method. The first pilot test was designed with undergraduate medical and nursing students in their fourth year. Their suggestions led the authors to use the Moodle platform of their University as a conductive thread. A second pilot with non-students led to the need for a website where the learners could find the videos with e-learning content. Three videos were seen during the practice time. The debate notes the need for having an official method of blended or virtual approaches to teach Cardiopulmonary Resuscitation (CPR) in an efficient and cost-saving way.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".