Understanding Toddlers' In-Home Injuries: II. Examining Parental Strategies, and Their Efficacy, for Managing Child Injury Risk
Bibliographic record
Abstract
Multimethod strategies (i.e., questionnaires, injury-event recording diaries, and telephone and home interviews) were used to study in-home injuries experienced by toddlers over a 3-month period and to identify anticipatory prevention strategies implemented by parents, on a room-by-room basis, that effectively reduced child injury risk. Three types of prevention strategies were used by parents: environmental (e.g., hazard removal, safety devices to prevent access), parental (e.g., increased supervision, parent modification of their own behavior to decrease injury risk for their child), and child based (e.g., teaching rules or prohibitions to promote safety), with parents often using a combination of these. Use of these strategies, and their efficacy to reduce injury risk, varied on a room-by-room basis. Nonetheless, two general conclusions are supported: (1) An emphasis on child-based strategies never decreases, and often elevates, risk of injury to toddlers; and (2) parental and environmental strategies, either singularly or in combination, serve protective functions that significantly reduce children's risk of in-home injury. Although it is commonplace for parents of children between 2 and 3 years of age to transition from environmental and supervision strategies to the use of teaching and rule-based ones to manage injury risk, doing so too early clearly elevates children's risk of injury in the home.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".