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Record W4396692168 · doi:10.56771/jsmcah.v3.82

Unleashing insights from Toronto Humane Society’s urgent care fostering program: a community case report

2024· article· en· W4396692168 on OpenAlexaffabout
Jacklyn J. Ellis, Dillon Dodson, Larisa Nagelberg, Rachel H. Bedder

Bibliographic record

VenueJournal of Shelter Medicine and Community Animal Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsWildlife Conservation Society Canada
Fundersnot available
KeywordsPolitical sciencePublic administrationSociologyPublic relationsEngineering ethicsEnvironmental planningEnvironmental ethicsGeographyEngineering

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.117
GPT teacher head0.419
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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