Helmet Use for the Prevention of Brain Injuries in Motorcycle Accidents
Bibliographic record
Abstract
The results of numerous studies show the effectiveness of helmets in avoiding or reducing the severity of injuries in a motorcycle accident (Hundley et al., 2004; Keng, 2005; La Torre, 2003; Leon & Hernandez, 2004; Liu et al., 2004). Despite the proven effectiveness of helmets in avoiding or reducing the severity of brain injuries and legislation requiring their use by both motorcycles drivers and passengers in Spain since 1992, research has found that 29% of those killed in motorcycle accidents in 2007 were not wearing a helmet at the time of the accident (Spanish Interior Ministry, 2008). Similar legislation exists in most European countries. One model that can be used to predict risk and prevention behaviour among drivers is Bandura and Walters (1963) socio-behavioural approach. According to this model, a large proportion of social learning takes place through observing the real actions of others and the consequences these have (Bandura, 1986) Social approval for a specific conduct may change a risk behaviour, principally among young people and adolescents. According to the sociobehavioural model, adolescents’ use of a helmet when riding a motorbike is related to their beliefs regarding friends and relatives’ use of the same protective headgear. Other theoretical approaches, such as Bayes (1992) illness prevention model, postulate that the immediate consequences of past conducts are the most relevant variables in predicting future behaviour. Adolescents will therefore tend to produce behaviours which have immediate positive consequences or avoid immediate negative consequences. Various different studies have identified a number of variables related to adopting preventive behaviours when driving, including: social influence (Bianco et al., 2005, State of Hawaii Department of Transportation, 2004; Canada Safety Council, 2006; Fuentes, 2007; Fuentes et al., 2010), belief in the effectiveness of the behaviour (Gras et al., 2007; Fuentes, 2007; Fuentes et al., 2010) and the immediate consequences of its use (Block, 2001; Chiliaoutakis et al., 2000; Cunill et al., 2004; Cunill et al., 2005). If we focus on gender, according to a recent study (Fuentes et al.,2010) young men ride motorcycles more frequently than young women (23.4% vs. 6.9%) (p <0.05) and eight out of every ten male and female adolescents say they always wear a helmet when riding a motorbike, with no differences by gender. The main reason adolescents who ride motorbikes wear a helmet is the safety it provides (87.2%), whereas there are three reasons for not wearing one: the characteristics of the
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".