Understanding Safety in Prehospital Emergency Medical Services for Children
Bibliographic record
Abstract
OBJECTIVE: For over a decade, the field of medicine has recognized the importance of studying and designing strategies to prevent safety issues in hospitals and clinics. However, there has been less focus on understanding safety in prehospital emergency medical services (EMS), particularly in regard to children. Roughly 27.7 million (or 27%) of the annual emergency department visits are by children under the age of 19, and about 2 million of these children reach the hospital via EMS. This paper adds to our qualitative understanding of the nature and contributors to safety events in the prehospital emergency care of children. METHODS: We conducted four 8- to 12-person focus groups among paid and volunteer EMS providers to understand 1) patient safety issues that occur in the prehospital care of children, and 2) factors that contribute to these safety issues (e.g., patient, family, systems, environmental, or individual provider factors). Focus groups were conducted in rural and urban settings. Interview transcripts were coded for overarching themes. RESULTS: Key factors and themes identified in the analysis were grouped into categories using an ecological approach that distinguishes between systems, team, child and family, and individual provider level contributors. At the systems level, focus group participants cited challenges such as lack of appropriately sized equipment or standardized pediatric medication dosages, insufficient human resources, limited pediatric training and experience, and aspects of emergency medical services culture. EMS team level factors centered on communication with other EMS providers (both prehospital and hospital). Family and child factors included communication barriers and challenging clinical situations or scene characteristics. Finally, focus group participants highlighted a range of provider level factors, including heightened levels of anxiety, insufficient experience and training with children, and errors in assessment and decision making. CONCLUSIONS: The findings of our study suggest that, just as in hospital medicine, factors at the systems, team, child/family, and individual provider level system contribute to errors in prehospital emergency care. These factors may be modifiable through interventions and systems improvements. Future studies are needed to ascertain the generalizability of these findings and further refine the underlying mechanisms.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".