Creation and validation of a roadside rescue skills scale for training pre-hospital medical teams: the RoadRes-Q scale
Bibliographic record
Abstract
BACKGROUND: Road traffic injuries are the leading cause of death among young people worldwide. While advances in vehicle safety have reduced some of the risks, the speed and quality of pre-hospital care are critical to prevent fatalities. In France, patients are cared for by medical teams and firefighters who must work together as closely as possible to ensure the best possible survival rate. However, there is a lack of standardised scales to assess the performance of these multidisciplinary teams. This study aimed to create and validate a roadside rescue skills assessment scale, the RoadRes-Q scale, for healthcare teams. METHODS: We used a two-round Delphi method to develop the RoadRes-Q scale. A panel of 9 international roadside rescue experts, including 7 firefighters and 2 engineers in road rescue equipment, agreed to participate. The scale covers five key areas: healthcare provider protection, site securing, vehicle securing, first aid delivery, and patient extrication. The final version was tested during two one-day simulation-based training sessions, each involving 22 participants: 6 healthcare staff, 14 firefighters, and 2 simulated victims. Assessors completed the scale during and after each scenario, focusing on internal consistency and inter-observer reliability. RESULTS: The RoadRes-Q scale consists of 60 items. Internal consistency was excellent (Cronbach's alpha of 0.86), indicating that items were non-redundant and consistently measured the required competencies. However, inter-observer reliability was low (intra-class correlation coefficient of 0.48), suggesting variability between assessors. Satisfaction among participants to the simulation-based training courses was high, and their knowledge increased. CONCLUSIONS: The RoadRes-Q scale proved to be a valid and reliable scale for evaluating both technical and non-technical skills. While internal consistency was strong, improvements are needed in inter-observer reliability. Structured training for assessors and video-based assessments could enhance reproducibility. The RoadRes-Q scale has the potential for assessing the quality and safety of care provided by healthcare teams in roadside rescue situations. REGISTRATION: As the study did not involve interventional research or patient participation, ethics committee approval was not required, but it received approval from the scientific referents of the Faculty of Medicine of Poitiers, and participants provided informed consent for using their anonymised data.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".