Effectiveness of breed-specific legislation in decreasing the incidence of dog-bite injury hospitalisations in people in the Canadian province of Manitoba
Bibliographic record
Abstract
BACKGROUND: The city of Winnipeg was the first among several jurisdictions in Manitoba, Canada, to introduce breed specific legislation (BSL) by banning pit-bull type dogs in 1990. The objective of the present work was to study the effectiveness of BSL in Manitoba. METHODS: Temporal differences in incidence of dog-bite injury hospitalisations (DBIH) within and across Manitoba jurisdictions with and without BSL were compared. Incidence was calculated as the number of unique cases of DBIH divided by the total person-years at risk and expressed as the number per 100000 person-years. Year of implementation determined the pre-BSL and post-BSL period for jurisdictions with BSL; for jurisdictions without BSL to date, the entire study period (1984-2006) was considered as the preimplementation period. The annual number of DBIH, adjusted for total population at risk, was modelled in a negative binomial regression analysis with repeated measures. Year, jurisdiction and BSL implementation were independent variables. An interaction term between jurisdiction and BSL was introduced. RESULTS: A total of 16 urban and rural jurisdictions with pit-bull bans were identified. At the provincial level, there was a significant reduction in DBIH rates from the pre-BSL to post-BSL period (3.47 (95% CI 3.17 to 3.77) per 100000 person-years to 2.84 (95% CI 2.53 to 3.15); p=0.005). In regression restricted to two urban jurisdictions, DBIH rate in Winnipeg relative to Brandon (a city without BSL) was significantly (p<0.001) lower after BSL (rate ratio (RR)=1.10 in people of all ages and 0.92 in those aged <20 years) than before (RR=1.29 and 1.28, respectively). CONCLUSIONS: BSL may have resulted in a reduction of DBIH in Winnipeg, and appeared more effective in protecting those aged <20 years.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".