Challenges of Transferring Burn Victims to Hospitals: Experiences of Emergency Medical Services Personnel
Bibliographic record
Abstract
A thorough understanding of experiences of Emergency Medical Services (EMS) personnel related to the field transfer of burn victims can be used as a prerequisite of quality improvement of pre-hospital clinical care for these kinds of victims. The aim of the present study was to explore the experiences of EMS personnel during transferring burn victims. In this qualitative research, content analysis was performed to explore the experiences and perceptions of a purposeful sample of Iranian EMS personnel (n = 32). Data collection continued until a point of saturation was reached. Data was collected using in-depth semi-structured interview and field observations and analyzed by qualitative inductive content analysis. After data analyzing from experiences of pre-hospital emergency personnel during transferring burn victims 7 subcategories were developed and classified into three main categories as challenges of transferring burn victim including; risks during patient transfer, restrictions in the admission of burn victims and uncertainties about patient referral. This study showed that different factors affect the quality of pre-hospital clinical services to the field transfer of burn victims that should be considered to improve the quality of pre-hospital clinical care of burn victims in dynamic programs. Further investigation is needed to explore the process of these crucial services.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".