Risk factors affecting length of stay of cats in an animal shelter: A case study at the Guelph Humane Society, 2011–2016
Bibliographic record
Abstract
There is a strong need for animal shelters to determine strategies to decrease the incidence of healthy animals being euthanized due to a lack of space. Thus, the Capacity for Care program was implemented at the Guelph Humane Society during August 2014. One objective of the Capacity for Care program is to decrease length of stay of cats within the shelter to improve individual welfare and increase the number of successful adoptions. The current study uses data collected from the Guelph Humane Society between 2011 and 2016. A Cox proportional hazards regression model was used to determine factors affecting a cat's time-to-adoption (length of stay). Cats' length of stay decreased by 24% after the implementation of the Capacity for Care program (p<0.001). Exotic breeds were found to have a length of stay 64% shorter than domestic shorthairs (p<0.01), while males had a length of stay 20% shorter than females (p<0.001). Adult cats' length of stay was 13% shorter than kittens (p≤0.01), which is expected as a result of calculating length of stay from date of admission rather than date available for adoption. This study provides evidence that the Capacity for Care program is associated with a reduced length of stay for cats; suggesting it is a suitable program to improve cat welfare and address cat overpopulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".