Healthy Lifestyle Behaviors of University Students
Bibliographic record
Abstract
person.What is witness event doing?What are the main causes of absence of first aid?Background: The ability of carrying out first aid should be one of basic skills in each of us.Methods: The study was conducted in the third quarter of 2015, on 335 members of emergency medical teams: doctors, paramedics, nurses and drivers, who provided medical rescue activities in the Provincial Ambulance and Health Transport "Meditrans" in Warsaw.The research tool was an anonymous questionnaire.Results: The straight majority of respondents encountered granting first aid at the event on the spot, however, the frequency of appearing of such proceedings isn't too high.First aid has most often been given on the street and in houses.Middleaged persons constituted the most numerous group that first aid was administered to.The important element is the reason not to provide first aid by bystanders, in the opinion of members of the ambulance.The most common answers were: fear, lack of knowledge and skills, reluctance, indifference, lack of training, lack of experience and fear of doing harm.Conclusion: By far the majority of respondents meets with the carrying of first aid, but respondents assessed the low incidence of such situations.Placing the victim in the recovery position is the simplest and most common form of the provision of first aid.Organized training in first aid should be extended to activities carried out in other situations, other than cardiac arrest.Only continuous education and in particular the practical training, will help people to overcome the barrier of their limitation in order to help others.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".