A Qualitative Review of the Air Rescue One Rural Search and Rescue Program in British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: Rural emergency prehospital care in British Columbia is conducted primarily by the British Columbia Ambulance Services or ground search and rescue volunteers. Since 2014, the volunteer Air Rescue One (AR1) program has provided helicopter emergency winch rescue services to rural British Columbia. The aim of this research was to describe the activity of the AR1 program and to make recommendations to improve future operations. METHODS: Data were collected retrospectively from September 2014 to May 2021, and parameters of emergency callout statistics from the organization's standard operating guidelines, rescue reports, and interviews were summarized and reviewed. RESULTS: Of 152 missions within the study period, 105 were medically related rescues involving trauma or cardiac events. Snowmobiling, mountain biking, and hiking were the most common activities requiring rescue. The 38 medical callouts that were not completed by AR1 were reviewed for contributing factors. Response time varied due to the vast service area, but median time from request to takeoff was 55 min (interquartile range 47-69 min), and median on-scene time was 21 min (interquartile range 11-33 min). CONCLUSIONS: AR1 provides advanced medical care into British Columbia's remote and difficult-to-access areas, minimizing delays in treatment and risk to patients and responders. Callout procedures should be streamlined enabling efficient AR1 activation. Collection of medical and flight information should be improved with standardized documentation, aiding in internal education and future research into the program's impact on emergency prehospital care. Future directions for improvement of care include the possibility of introducing portable ultrasound technology.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".