Unintentional poisoning exposures: how does modeling the opening of child-resistant containers influence children’s behaviors?
Bibliographic record
Abstract
OBJECTIVE: Unintentional poisoning in the home is a risk for children. Over-the-counter medicinal products in child-resistant containers (CRC) are common causes of pediatric poisoning. The current study examined children's abilities to open three types of CRC mechanisms (twist, flip, and push) and corresponding control containers, comparing their ability to do so spontaneously and after explicit modeling. The study also examined if inhibitory control (IC) was associated with children's overall score for spontaneous openings. METHOD: Children 5-8 years old were randomly assigned to one of three mechanism conditions (between-participants factor): twist, flip, and push, with each child experiencing both a risk and a control container (within-participants factor) having that mechanism. Children were first left alone with a container (measures: engagement with container, spontaneous opening) for up to 2 min and subsequently observed an adult explicitly model opening the container before the child was asked to do so (measure: opening after modeling). RESULTS: Children were more engaged with and likely to spontaneously open control containers than CRCs, though some (4%-10%) also opened CRCs. After modeling, significantly more children opened each of the three types of CRCs, with nearly all children opening the push mechanism CRC. IC positively predicted children being more engaged with and spontaneously opening more containers. CONCLUSIONS: Implications for improving pediatric poison prevention 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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".