Bibliographic record
Abstract
Road trauma is a leading cause of child injury worldwide. In highly motorized countries, injury as a passenger represents a major proportion of all child road deaths and hospitalizations. Australia is no exception, particularly because there are high levels of travel by private motor vehicle to school in most Australian states. Recently, the legislation in Australia governing the type of car restraints required for children younger than 7 years of age has changed and has aligned requirements better with accepted best practice. However, it is unclear what effect these changes have had on children's seating positions or the types of restraints used. A mixed-methods evaluation of the impact of the new legislation on compliance was conducted at three times: baseline (Time 1), after announcement that changes were going to be implemented but before enforcement began (Time 2), and after enforcement commenced (Time 3). Measures of compliance were obtained by two methods: roadside observations of vehicles with child passengers and parental self-report (intercept interviews conducted at Times 2 and 3 only). Results from the observations suggested an overall positive effect. Proportions of children occupying front seats decreased overall, and use of dedicated child seats increased to almost 40% of the observed children by Time 3. However, almost a quarter of the children observed still occupied front seats. These results differed from those of the interview study, in which almost no children were reported as usually traveling in the front seat, and reported use of dedicated restraints with children was almost 90%, more than twice that of the rate in observations.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".