Changes in parents' perceived injury risk after a medically-attended injury to their child
Bibliographic record
Abstract
Unintentional injuries are a major cause of hospitalization and death for children worldwide. Since children who sustain a medically-attended injury are at higher risk of recurrence, it is crucial to generate knowledge that informs interventions to prevent re-incidence. This study examines when, in the year following a medically-attended injury, parents perceive the greatest risk of injury recurrence. Since perception of injury risk is associated with parental preventive behavior, this can inform decisions on the timing of parent-targeted interventions to prevent re-injury. Study participants were 186 English-fluent parents of children 0 to 16 years, presenting at the British Columbia Children's Hospital for an unintentional pediatric injury. Parents were excluded if their child had a disability or chronic health condition. Perceived risk of the same and of any injury recurring were elicited from parents, when they sought treatment at the hospital, as well as one, four, and twelve months later. The study ran between February 2011 and December 2013. Mixed-effects models were used to analyze changes in parents' responses. Analysis indicates that perceived risk of the same injury recurring did not change. However, perceived risk of any injury recurring increased from baseline to first follow-up, then decreased during the rest of the year. Overall, perceived risk of any injury was higher for parents whose child had a history of injuries. Visits to the Emergency Department for a pediatric injury may not be optimal timing to deploy injury prevention interventions for parents. Follow-up visits (when parents' perceived risk is highest) may be better.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".