First-time mothers’ responses to young children behaving in ways that can lead to injury
Bibliographic record
Abstract
OBJECTIVES: Children of first-time mothers experience an elevated risk of unintentional injuries, though little is known about the safety practices of first-time mothers. The current study aimed to identify factors that influence how first-time mothers respond to young children engaging in injury-risk behaviors in the home. Based on past research regarding influences on non-first-time mothers' safety practices, factors considered herein included: demographic attributes, parent personality, parenting style, and family context. METHODS: : First-time mothers (N = 74) of young children (M = 24.39 months) were exposed to hypothetical scenarios in which a child's behavior could be interpreted in terms of misbehavior or threats to their safety; mothers were advised to react as if the child were their own. RESULTS: Analysis of verbatim responses made by mothers to their children indicated that first-time mothers were significantly more concerned with correcting undesirable behaviors than addressing safety risks. High scores on parent permissiveness and conscientiousness were protective, predicting higher frequencies of teaching/explaining responses about safety, which has been shown to moderate children's injury-risk behaviors. In contrast, greater household chaos and lower conscientiousness were risk factors, predicting more power-assertive responses, which are ineffective for stopping children from engaging in injury-risk behaviors. CONCLUSION: : Injury prevention interventions may benefit first-time parents by assisting them to enact strategies that teach young children about safety, reduce power-assertive parenting, and moderate the level of household chaos. Suggestions for future research are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".