The Lifeguard Rescue Reporting System: Survey Results from a Collaborative Data Collection Method
Bibliographic record
Abstract
Several water safety organizations have attempted to improve reporting regarding lifeguard actions in order to better understand the characteristics of successful, non-fatal rescues. In 2003, a collective effort initiated the Lifeguard Rescue Reporting System, an online survey distributed to lifeguards and facility managers across the United States and Canada to better understand rescue actions performed in pools/spas, water parks, and open water areas. After seven years of data collection, the online survey accumulated data reflecting 1,676 rescue actions, collecting information including location, victim characteristics and outcome, rescuer characteristics and strategies, and other general circumstances. Descriptive results indicated that at least half of victims were 14 years old or younger across all settings. Depths of 0.9-1.5m (3-5 ft) represented the range at which incidents most frequently occurred in pools and spas and waterparks, whereas the depth of incidents was generally deeper in natural and open waterways. During rescue incidents, water safety personnel generally identified victims either visually (83-92% of the time) and/or audibly (18-29%), although victim “profiling” was also employed 10-14% of the time to identify at-risk swimmers. Notably, across all three water setting types, no medical aid was required in most cases (60-72%), suggesting the efficacy and essentiality of lifeguards as aquatic first responders. Accordingly, as water-based recreation maintains its popularity, systematically collecting and analyzing data specific to everyday, rescue actions are critical to improving lifeguard education and strategic, data-based operating procedures.
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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 | 0.041 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".