Can intake data inform on impacts of repeated subsidized onsite spay-neuter clinics for dogs?
Bibliographic record
Abstract
Objective: Subsidized dog care and population management programs (DPM) are often implemented for dog population control where for-profit veterinary care is inaccessible. However, impacts of such programs are rarely assessed. The goal of this project was to determine if and how previously collected intake data from ongoing high-volume spay-neuter clinics could be used to measure impacts of such DPM programs. Animals: We used intake data collected from 2008 to 2019 from spay-neuter clinics that had been delivered repeatedly over a 10-year period in 6 First Nations communities in Alberta, to assess changes in intake dog characteristics. Procedures: Numbers of dogs brought in for spay-neuter surgery or surrendered, and their ages, sexes, breeds, weights, and body condition scores were compared. Reasons for surrender were investigated and socioeconomic factors were investigated as possible drivers for community differences in clinic participation rates and clinic sex ratios. Results: < 0.05), with large-breed dogs always being the most common. However, there was an increase in small and x-small breeds in the final years of the study. Finally, weight status, a calculated estimate of body condition of spay-neuter dogs, increased over time within large and medium breeds, whereas the body condition score of surrendered dogs shifted from "thin" to "ideal" over time. Conclusions and clinical relevance: This work highlights the potential and challenges of using intake data to assess impacts of spay-neuter clinics that occur repeatedly over many years in the same communities. Overall increased weight status, high participation rates, and reduction of age at intake suggest positive effects of the assessed spay-neuter programs.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".