The absence of data on driving under the influence of alcohol in road traffic studies: a scoping review of non-randomized studies with vote counting based on the direction of effects of alcohol policies
Bibliographic record
Abstract
BACKGROUND: Data on driving under the influence of alcohol (DUIA) are not always available, accurate, or reliable, making it difficult to study the effects of alcohol policies on road traffic outcomes. The objectives of our study were twofold: 1) to describe how road traffic outcomes of alcohol policies are assessed when DUIA data are missing, and 2) to explore the effects of alcohol policies when DUIA data are missing. METHODS: We conducted a scoping review of non-randomized studies that assessed the road traffic outcomes of alcohol policies when DUIA data are missing. Until November 2021, we searched studies published between 2000 and 2021, in English or French, via MEDLINE, APA PsycInfo, CINAHL, and SocINDEX. We assessed the risk of bias in the included studies with the Quality Assessment Tool for Before-After (Pre-Post) Studies With No Control Group. The selection process, data extraction, and the risk of bias assessment were conducted independently and in duplicate. We used vote counting based on the direction of the effects of alcohol policies as a synthesis method. The protocol for this review was published in PROSPERO under record number CRD42021266744. RESULTS: Twenty-four eligible studies were included. Regarding objective 1, most studies used uncontrolled interrupted time series designs to assess road traffic fatalities resulting from night-time crashes. The reasons for missing DUIA data were generally not reported. Regarding objective 2, we found evidence for an association between alcohol policies and decreased road traffic fatalities. Subgroup analyses found no evidence for an association between methodological modifiers and positive effect directions for road traffic fatalities. CONCLUSION: Caution is needed when interpreting road traffic outcomes associated with alcohol policies when DUIA data are missing. Greater efforts should be made to improve the reporting of outcomes assessments. Future studies must address several methodological issues (e.g., more granular data, well-defined intervention and implementation, and controlled designs). Our results should be compared to those from others reviews where DUIA data were available to confirm or recalibrate the associations found in studies where DUIA data were missing.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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 | 0.000 |
| 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".