Insights about the Epidemiology of Dog Bites in a Canadian City Using a Dog Aggression Scale and Administrative Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dog bites are a public health concern that also implicates animal welfare, with negative outcomes such as rehoming or euthanasia for the animals responsible. Previous research has shown that the severity of dog-bite injuries reflects multiple factors, including the degree of inhibition exhibited by dogs and how people behave towards dogs. This study utilizes an objective dog bite injury assessment tool: The Dunbar aggression scale. Trained officers employed by The City of Calgary systematically use the Dunbar scale whenever investigating dog-bite complaints. We analyzed The City of Calgary's administrative data on confirmed dog-bite injuries in people, 2012-2017, with a multivariable generalized ordered logistic regression model. Severe dog-bite injuries occurred more frequently in the family home than in any other setting. Young children, youths and older adults were at higher risk of more serious bites than adults. There has been a decreasing trend in the probability of a high or medium severity bite, and an increasing trend in the probability of a low severity bite since 2012. These results indicate that greater public awareness regarding dog-bite injuries is needed. Consideration should be given to campaigns targeted towards different demographics, including older adults, to provide an understanding of dog behaviour and to emphasize the need to supervise children closely in the presence of all dogs at all times, including family dogs in the home environment. Given that dog-bite injuries are not just a public health issue, but also an animal welfare issue, we endorse One Health responses in educational campaigns, policy development, and professional practice.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it