In Search of a Standard for Assessing the Crash Risk of Driving Under the Influence of Drugs Other Than Alcohol; Results of a Questionnaire Survey Among Researchers
Bibliographic record
Abstract
OBJECTIVE: To find a gold standard for crash risk assessment studies in the field of driving under the influence of psychoactive substances. METHODS: A questionnaire survey on methodological aspects concerning study designs was sent to researchers in the field of driving under the influence of psychoactive substances. The questionnaire was aimed at the 4 main study designs to assess the crash risk of driving under the influence: case-control studies, culpability studies, pharmaco-epidemiological studies, and experimental studies. RESULTS: The response rate for the questionnaire was 68 percent (N = 57). Forty-six percent of the respondents had a preference for assessing the crash risk by means of case-control studies, 35 percent by means of experimental studies, 14 percent by means of culpability studies, and 5 percent by means of pharmaco-epidemiological studies. In practice, however, only 51 percent of the researchers actually used the study type they preferred in theory. For the 4 most commonly used study designs, similarity rates varied from 66 to 81 percent for the theoretically preferred design and from 52 to 77 percent for the design that was actually applied. CONCLUSIONS: Based on the results of the questionnaire survey, it can be concluded that despite several attempts in the past to standardize study design, there is still no common standard for assessing the crash risk of driving under the influence. The differences are not only caused by practical, legal, financial, or ethical issues but also by differences between researchers concerning their theoretically preferred study design.
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 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 | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".