Unleashing insights from Toronto Humane Society’s urgent care fostering program: a community case report
Bibliographic record
Abstract
The Urgent Care (UC) fostering program at the Toronto Humane Society (THS) supports individuals experiencing crisis situations (housing instability, fleeing interpersonal violence, or undergoing healthcare treatments), by providing a no-cost fostering service for their animal(s). All applications to THS’s UC program between January 1, 2020 and October 1, 2022 and all successful admissions to the program during this period were included in this study. There were 358 admissions of 328 unique animals, from 244 families. Seventy-four percent (n = 265) of admitted animals were reunited with their owner. The highest rates of reunion at the end of the program were linked to dogs, requests in support of people fleeing interpersonal violence, consistent or frequent communication with the client, and not requiring veterinary or behavioral care/training beyond standard levels. THS’s UC program presents an inexpensive and effective way to help support people undergoing temporary crises, preserving the human–animal bond, which may help them heal in the aftermath of these crises, and prevent the needless relinquishment of animals to shelters. Through writing this report, opportunities have been identified for improving the program to better serve our community, and details have been provided that might help other organizations operating or planning to launch a similar program.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".