MétaCan
Menu
Back to cohort

Risk factors affecting length of stay of cats in an animal shelter: A case study at the Guelph Humane Society, 2011–2016

2017· article· en· W2765661810 on OpenAlexaffabout
Natasha Janke, Olaf Berke, Tyler Flockhart, Shane Bateman, Jason B. Coe

Bibliographic record

VenuePreventive Veterinary Medicine · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCATSEnvironmental healthDemographyMedicineVeterinary medicineSociologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.072
GPT teacher head0.425
Teacher spread0.353 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations38
Published2017
Admission routes2
Has abstractyes

Explore more

Same venuePreventive Veterinary MedicineSame topicHuman-Animal Interaction StudiesFrench-language works237,207