Parents' knowledge about and use of child safety systems.
Bibliographic record
Abstract
Road crashes are the leading cause of death and injury in children under 14 years of age in Canada, despite mandatory use of vehicle restraints. A survey design was used to examine parental knowledge and perceptions of the use of safety systems for children in 2 communities in the province of Ontario. Parents of children aged newborn to 9 years were recruited from 3 urban/rural school boards and from daycare centres and hospitals. A total of 1,263 parents reported on 2,199 children's use of safety systems. Data analysis revealed that only 68% of children used correct seats for their weight and that as the child advanced in age the rate of misuse increased significantly due to high rates of premature transitioning into safety seats inappropriate for the child's height and weight. The results also revealed that parents had limited knowledge concerning the correct use of safety seats and frequently used non-professional sources of information for vehicle safety information. The authors recommend that nurses develop a comprehensive and systematic strategy to ensure that families understand how to secure children in vehicles using the correct safety seat for the child's height, weight, and age.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".