Bibliographic record
Abstract
It is well established that falling asleep is a critical factor in approximately 10–30% of driving accidents, and probably an even more significant contributor to fatal accidents. Analyses of the circumstances in which this occurs have identified time of day (greater risk at night), age (higher risk when young) and duration of driving (higher risk with longer driving bouts) as important determinants – so why is a publication on ‘Sleepiness at the wheel across Europe: a survey of 19 countries’ (Gonçalves et al., 2015) of any news interest? The study documents the responses to a survey in which participants volunteered information on falling asleep while driving during the past 2 years and factors that may have contributed to this. The overall period prevalence averaged across the participating countries is estimated at 17%, which is a staggering percentage that is in accordance with other estimates. The prevalence of accidents associated with falling asleep is 7%, with accidents involving fatalities estimated at 3.6%. Interesting between-country differences in the prevalence of falling asleep at the wheel emerge: 34.7% in the Netherlands to 6.1 in Croatia, but remain unexplained. A better understanding of the between-country differences is needed urgently. Does it relate to differences in awareness, policies, sleeping habits or traffic characteristics? The identified ‘risk factors’ confirm previous reports and include time of day, age, etc. Two aspects are worth mentioning. Forty-two per cent of falling-asleep events are attributed to poor sleep during the previous night and/or poor sleep in general (34%). This self-reported awareness clearly provides opportunities for a change in behaviour. Furthermore, the STOPbang questionnaire-derived risk for obstructive sleep apnea (OSA) (University Health Network, Toronto, ON, Canada) was related to the risk for falling asleep. European legislation will require that the OSA risk of all driving licence holders, i.e. commercial and non-commercial drivers, are assessed and that those with moderate to severe sleep apnea are treated adequately before being allowed to drive. Although this is to be welcomed, we should not forget that most incidents of falling asleep at the wheel appear not to be related to OSA but to other factors, including modifiable lifestyle factors. The take-home message must be: ‘don't drive when you feel sleepy, whatever the cause may be’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".