Mothers' Home-Safety Practices for Preventing Six Types of Childhood Injuries: What Do They Do, and Why?
Bibliographic record
Abstract
OBJECTIVE: To identify determinants of mothers' home-safety practices for preventing six types of common injuries to children (burns, poisoning, drowning, cuts, strangulation/suffocation/choking, and falls). METHODS: Home interviews were conducted with mothers of children 19-24 and 25-30 months old about home-safety practices. For each of 30 safety precautions to prevent these six types of injuries, mothers indicated whether or not they engaged in the practice, and explained why. RESULTS: Regression analyses revealed both common and unique determinants of mothers' home-safety practices to prevent these six types of home injuries. For burns, cuts, and falls, beliefs that child characteristics and parent characteristics elevated the child's risk of injury were the key determinants of the mother's engaging in precautionary measures. For drowning, poisoning, and suffocation/strangulation/choking, health beliefs also contributed to predict mothers' practices, including beliefs about potential injury severity and extent of effort required to implement precautionary measures. CONCLUSIONS: The factors that motivated mothers to engage in precautionary measures at home varied depending on the type of injury. Intervention programs to enhance maternal home-safety practices will need to target different factors depending on the type of injury to be addressed.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".