Inhibitory control and children's risk for dog bites
Bibliographic record
Abstract
Introduction Approximately 400 000 US children seek medical attention for dog bites annually (CDC, 2008). A dozen die. Inhibitory control, the capacity to plan and suppress inappropriate approach responses under instruction or in novel situations (Rothbartet al, 2001), is linked to broad child injury risk (Schwebel & Plumert, 1999), but is poorly understood as a risk factor for pediatric dog bites. Method 82 children (mean 4.84 years; SD=0.91, 46% male) were recruited from Birmingham, USA and Guelph, Canada. All lived in homes with 1–3 dogs. Parents completed the inhibitory control scale of the Children's behaviour questionnaire (Rothbartet al, 1994) and items concerning children's history with dogs. Results Inhibitory control correlated to parent-report of children's history of almost being bitten by a dog, r=−0.27; uninhibited children were more likely to have almost experienced dog bites. Inhibitory control also related to parent-report of children's negative experiences with dogs that resulted in crying, r=−0.29, (uninhibited children more likely to have had negative incidents); of children playing rough with dogs, r=−0.31 (uninhibited children playing rough with dogs); and of parent concern that children's behaviour will anger their dog, r=−0.25 (parents of uninhibited children more concerned). Implications Current dog bite prevention strategies focus on dog control and owner education (eg, leash laws, animal training). These strategies help, but identification of child specific risk factors, such as inhibitory control, could aid development of other dog-bite prevention programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".