Patient Outcomes in Helicopter Emergency Medical Service Documentaries and on Air Ambulance Websites
Bibliographic record
Abstract
Background Helicopter emergency medical service (HEMS) documentaries attract millions of viewers, and publicly available patient stories on Air Ambulance websites are vital to raise awareness and funding for Air Ambulance charities in the United Kingdom (UK). Despite abundant research investigating how fictional programs and news outlets present patient health outcomes, there are no comprehensive studies that investigate how non-fictional HEMS documentaries or Air Ambulance websites present patient outcomes. The aim of this study is to capture the frequency of poor outcomes (mortality) in patients broadcasted on documentaries focusing on HEMS and the patient stories section of UK Air Ambulance websites. Methods A retrospective cohort study reviewed five HEMS documentaries between January 2016 and October 2019 and 20 Air Ambulance websites that had patient stories published until October 2020. In all, 628 patients identified fit the eligibility criteria: 311 from HEMS documentaries and 317 patients from Air Ambulance websites. Results In all, 0.64% (4/628) of patients died before the hospital, including 0.96% (3/311) of patients on HEMS documentaries and 0.32% (1/317) of patients on Air Ambulance websites. In addition, 2.23% (14/628) of patients died according to their final mention in the data source, including 1.93% (6/311) of patients on HEMS documentaries and 2.52% (8/317) of patients on Air Ambulance websites. Conclusions This study suggests under-reporting of poor patient outcomes in HEMS documentaries and on UK Air Ambulance websites. This could be attributed to the logistical and ethical implications of capturing and presenting poor outcomes but likely impacts upon public perception. Medical professionals should recognize this in order to proactively address potential misconceptions when communicating with patients and their families.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".