Factors associated with booster seat use among Indigenous peoples in Canada
Bibliographic record
Abstract
Booster seat use among the general population remains relatively low, despite their effectiveness in preventing injury among children when involved in motor vehicle collisions. Given the prevention of injuries that booster seats provide, understanding the factors that hinder or facilitate the use of these seats is critical, particularly in communities that are often overlooked when conducting general population studies. To date, no studies have examined the prevalence and predictors of booster seat use among Indigenous peoples in Canada. The purpose of this study was to examine the use of booster seat use among Indigenous peoples across Canada and the factors that impact their use. Data were collected from a survey of participants from First Nations communities and organizations serving Indigenous peoples nationwide. Hypotheses arising from known predictors of booster seat use across the general population were tested using logistic regression models. The strongest predictor of booster seat use, even when all other study factors were accounted for, was the reduction of barriers related to the use of booster seats, such as a child’s resistance to being placed or staying in the passive safety restraint or a parent, guardian, or other caregiver being unwilling to use or unsure of how to install/setup the booster seat. Most Indigenous participants consistently used booster seats to safely secure children being transported in vehicles. However, this compliance rate is well below that of the general population. Accessibility and affordability of child safety restraints and/or children’s refusal to use booster seats, as well as having more than 1 child to secure, were identified as mitigating factors. Access to and the affordability of booster seats, coupled with clear and understandable information on how to use them, are critical components to compliance. Raising awareness among Indigenous peoples communities regarding the importance of using booster seats is imperative. To achieve this, Indigenous peoples must lead discussions to ensure that child safety strategies not only are based on research and best practices but are culturally connected and community driven. Through meaningful collaboration, vehicle-related injuries and mortality among Indigenous children can be significantly reduced.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".