Latent Hazard Anticipation in Young Drivers: Review and Meta-Analysis of Training Studies
Bibliographic record
Abstract
Young drivers are overrepresented in motor vehicle crashes, and are shown to be poorer at anticipating potential threats on the roadway compared with their more experienced peers. Literature demonstrates the effectiveness of driver training programs at improving young drivers’ latent hazard anticipation performance. Various hazard anticipation training studies have been undertaken on different population demographics using different training scenario presentation modes and multiple evaluation testbeds. These error-based feedback training programs (3M) allow trainees to make a mistake, show them how to mediate the mistake, and provide an opportunity to master the target skills. The current meta-analytical review focused on 19 peer-reviewed training studies that utilized eye movements to measure improvements in drivers’ latent hazard anticipation performance following training. The role of four moderating factors (mode of delivery – PC-based or non PC-based; presentation of training – egocentric or exocentric; method of evaluation – on-road or driving simulator; and age of sample – teen novices aged 16–17 or young drivers aged 18–21) on the training effects were explored. Overall, the current meta-analysis suggest that: (a) superficial improvements in training programs does not necessarily further improve the drivers’ latent hazard anticipation; (b) drivers who completed a training program with both egocentric and exocentric training views achieved greater levels of latent hazard anticipation performance than those who completed a training program that contained either view, but not both; and (c) the effect sizes of the 3M-based training programs on latent hazard anticipation were greater for drivers aged 18–21 years than drivers aged 16–17.
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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".