Gaps in Education: A Cross-Sectional Survey Study of Knowledge of Advanced Lifesaving Interventions among Canadian Lifeguards
Bibliographic record
Abstract
Objective: The aim of this study was to assess lifeguards’ knowledge retention of airway management, oxygen administration, and ventilation interventions following certification and employer-provided training. Methods: This cross-sectional study was conducted using an online survey administered between February and May 2024. A total of 1322 responses from Canadian lifeguards certified in airway management and oxygen administration were deemed eligible for analysis. The survey included 15 knowledge assessment questions, with data analyzed based on lifeguard experience and the date of last certification or in-service training. Results: The mean knowledge assessment score was 10.4 ± 2.2 (69.3 ± 14.6%), with the highest scores in the airway management category and the lowest in the oxygen administration category. Lifeguard experience significantly increased knowledge retention, whereas recertification showed no significant impact, and employer-provided training significantly decreased knowledge retention. Conclusions: The findings underscore the importance of lifeguarding experience in knowledge retention among lifeguards. Optional airway management and oxygen administration recertification, coupled with inconsistent in-service training, have created significant gaps in lifeguard education. This study identifies the need for regular, competency-based training delivered by qualified facilitators. Addressing these gaps is crucial for enhancing the effectiveness of lifeguards in emergency response and ensuring high-quality care for drowning victims.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".