Basic life support awareness among non-medical students at King Faisal University, Al Ahsa, Saudi Arabia
Bibliographic record
Abstract
Background: The knowledge and skills about basic life support (BLS) and advanced life support are the most important determining factors of the cardiopulmonary resuscitation (CPR) success rates. Practicing simple CPR techniques as well as knowing BLS improves the chances of survival of the patient until experienced medical help can arrive. In most cases, it is sufficient for survival in itself. The study aimed to determine non-medical undergraduate students' knowledge of BLS and related skills at King Faisal University.Methods: A descriptive cross-sectional study was conducted across King Faisal University, Al Ahsa, Saudi Arabia between October 10 and December 30, 2021. A total of 406 students from nonmedical colleges participated in the study. A validated Arabic-language questionnaire was subsequently administered, which included 10 items assessing knowledge about BLS.Results: A total of 406 participants completed the questionnaire. The majority of participants (82.5%) had poor knowledge of the BLS. A quarter of students (25.1%) indicated that they had previously taken BLS training. Approximately (16%) of students acquired their knowledge about BLS from the internet, 7.6% from watching movies and TV shows, 16% from school subjects, 2.2% from college subjects, and 26.4% from reading.Conclusions: BLS knowledge was very limited among non-medical colleges students. It is evident from the study that nonmedical students need better BLS training in order to respond appropriately to cardiac arrest and other emergency situations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".