Factors Affecting the Likelihood of Dogs and Cats Returning to Their Owners at a Municipal Animal Shelter in the United States
Bibliographic record
Abstract
Introduction: Animal shelters consider return-to-owner (RTO) as an ideal outcome for animals, owners, and shelters. Methods to increase RTO likelihood are frequently discussed by shelter professionals nationwide. Some of these methods are evidence-based, while others are anecdotally successful. This retrospective study aimed to provide evidence for commonly suggested methods, as well as identify additional factors influencing RTO likelihood. Methods: Data from 5,960 dog and 3,489 cat impounds were obtained from a large municipal animal shelter in Utah, USA. Directed acyclic graphs were developed to visualize causal assumptions, which were used to identify confounders for adjustment in the logistic regression while modeling the associations between study variables and RTO outcomes for both dogs and cats. Results: Dogs and cats with microchips, older animals, healthy animals, neutered animals, and animals brought to the shelter via another public agency were more likely to return to their owners. Animal sex and season of impound did not affect either dogs’ or cats’ RTO likelihood. Conclusion: The characteristics influencing RTO likelihood were similar for both dogs and cats. These influences provide support for existing shelter practices, such as facilitating widespread microchipping and waiving reclamation fees, while also encouraging implementation of new practices, such as modifying stray hold periods based on source type or health status. Limitations of the study included the presence of incomplete information in the database and concerns with the generalizability of results to other shelters.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".