Structural Equation Modeling to Investigate Behavioral Factors Contributing to Crash Involvement: A Systematic Review
Bibliographic record
Abstract
Road traffic crashes are a leading cause of death among children and young adults. Research has shown that human behavior plays a significant role in these events. Among the methodologies used in that research for investigating the effect of different behavioral characteristics on traffic crashes, Structural Equation Modeling (SEM) is prominent due to its ability to analyze different data types. With this in mind and given the importance of understanding the contribution of human behaviour in traffic crashes, this study aims to systematically review the studies that used SEM to provide that understanding. In this paper, we present a detailed review of 19 articles retrieved from available digital libraries through keyword search, title screening, and screening for developed models. Based on the defined categories regarding the independent variables, sleep and risky driving were found to have the greatest influence on crash involvement. This and other results of this study help to highlight important directions in developing countermeasures for mitigating the undesirable effects of behavioral characteristics on road safety.
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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".