Overcoming distance: an exploration of current practices of government and charity-funded critical care transport and retrieval organizations
Bibliographic record
Abstract
BACKGROUND: For critically ill and injured patients, timely access to definitive care is associated with a reduction in avoidable mortality. Access to definitive care is significantly affected by geographic remoteness. To overcome this disparity, a robust critical care transport (CCT) or retrieval system is essential to support the equity of care and overcome the tyranny of distance. While critical care transport or retrieval systems have evolved over the years, there is no universally accepted system or standard, which has led to considerable variation in practices. The objective of this mixed-methods study was to identify and explore the current clinical, operational, and educational practices of government and charity-funded critical care transport and retrieval organizations operating across access- and weather- challenged geography. METHODS: This study utilized a mixed-methods approach comprising a rapid review of the literature and semi-structured interviews with identified subject matter experts (SME). RESULTS: A total of 44 articles and 14 interviews with SMEs from six different countries, 12 different services/systems, and seven operational roles, including clinicians (physician, paramedic, and nurse), educator, quality improvement, clinical governance, clinical informatics and research, operations manager, and medical director were included in the narrative analysis. The study identified several themes including deployment, crew composition, selection and education, clinical governance, quality assurance and quality improvement and research. CONCLUSION: This mixed-methods study underscores the paucity of literature describing current clinical, operational, and educational practices of government or charity-funded CCT or retrieval programs operating across access- and weather- challenged geography. While many common themes were identified including clearly defined mission profiles, use of dedicated or specialized transport teams, central coordination, rigorous selection processes, service-sponsored graduate education, and strong clinical governance, there is little consensus and considerable variation in current practices. Further research is needed to identify and harmonize best practices within the CCT and retrieval environments.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".