The Lack of First Aid Skills as a Contributing Factor of High Road Traffic Fatality Ratesin the Russian Federation
Bibliographic record
Abstract
Background: The Russian Federation is among the countries with the highest road traffic fatality rates in the WHO European Region. One of the main reasons for that is the personal attitude of traffic participants to safety that includes medical illiteracy and unpreparedness to render first aid to the injured. Our objective was to study failure to give quick and competent first aid as one of the possible causes of road traffic deaths in the Russian Federation and to identify potential ways of improving the situation. Materials and methods: In May-September 2018, in January and June 2019, we conducted a survey of 403 Russian citizens (284 men and 119 women) having a driver’s license and driving experience in different Russian regions including the Volgograd Region, the Krasnodar Krai, the Rostov Region, Moscow, etc. The respondents were asked to answer the questions about first aid training courses in driving schools, prior involvement in road traffic accidents, and first aid techniques. Results: Most of the respondents (63%) reported having had first aid training courses at driving schools given by driving instructors and almost a quarter of the respondents (23%) reported having never had such courses at all. Approximately 68% of the survey participants had no first aid training using dummies. About 17% witnessed traffic collisions and provided at least some level of first aid while almost 14% could not give any help due to the stressful situation or fear to do harm to the injured. We then calculated the number of correct and complete answers rated 1 and 2 points according to the estimation scale and drew a control chart of the average value. The analysis of the control chart showed that 331 of 403 participants (82%) passed our test. Conclusions: Although the results were not as bad as might have been expected, significant gaps in basic first aid knowledge did exist: some respondents failed to answer the questions correctly. Given that in addition to theoretical knowledge the person should be able to use this knowledge decisively and calmly, without panic and the fear of blood, the percentage of those who can really help is even lower. First aid training courses in driving schools shall be mandatory and must be given by medical professionals. Governmental regulatory authorities should pay special attention to this issue when licensing. At the same time, more emphasis should be placed on first aid training in educational institutions (kindergartens, schools, colleges, and universities). In addition, we recommend compulsory first aid training of professional drivers of commercial vehicles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".