How Parents Perceive the Potential Risk of a Child-Dog Interaction
Bibliographic record
Abstract
Dog attacks on children are a widespread problem, which can occur when parents fail to realise a potentially dangerous interaction between a dog and a child. The aim of the study was to evaluate the ability of parents to identify dangerous situations from several everyday child–dog interactions and to determine whether the participants connected these situations to a particular breed of dog. Five sets of photographs depicting potentially dangerous interactions from everyday situations between children and three dogs (one of each breed) were presented via an online survey to parents of children no more than 6 years old. Data from 207 respondents were analysed using proc GLIMMIX in SAS program, version 9.3. The probability of risk assessment varied according to dog breed (p < 0.001) as well as to the depicted situation (p < 0.001). Results indicated that Labrador Retriever was considered the least likely of the three dogs to be involved in a dangerous dog-child interaction (with 49% predicting a dangerous interaction), followed by Parson Russell Terrier (63.2%) and American Pit Bull Terrier (65%). Participants considered one particular dog-child interaction named ‘touching a bowl’ a dangerous interaction at a high rate (77.9%) when compared with the other presented situations, which were assessed as dangerous at rates of 48.4% to 56.5%. The breed of dog seems to be an influential factor when assessing a potentially dangerous outcome from a dog-child interaction. Contrary to our hypothesis, interactions involving the small dog (Russell Terrier) were rated more critically, similarly to those of the Pit Bull Terrier. These results suggest that even popular family dog breeds, such as Labrador Retrievers, should be treated with more caution.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".