A Multi-Site Feasibility Assessment of Implementing a Best-Practices Meet-And-Greet Intervention in Animal Shelters in the United States
Bibliographic record
Abstract
Animal shelters must incorporate empirically validated programs to increase life-saving measures; however, altering existing protocols is often a challenge. The current study assessed the feasibility of nine animal shelters within the United States to replicate a validated procedure for introducing an adoptable dog with a potential adopter (i.e., "meet-and-greet") following an educational session. Each of the shelters were first entered into the "baseline" condition, where introduction between adoptable dogs and potential adopters were as usual. After a varying number of months, each shelter entered into the "experimental" phase, where staff and volunteers were taught best practices for a meet-and-greet using lecture, demonstration, and role-play. Data on the likelihood of adoption following a meet-and-greet were collected with automated equipment installed in meet-and-greet areas. Data on feasibility and treatment integrity were collected with questionnaires administered to volunteers and staff followed by a focus group. We found that a single educational session was insufficient to alter the meet-and-greet protocol; challenges included not remembering the procedure, opposing opinions of volunteers and staff, lack of resources, and a procedural drift effect in which the protocol was significantly altered across time. In turn, no animal shelters increased their dog adoptions in the "experimental" phase. New research is needed to develop effective educational programs to encourage animal shelters to incorporate empirical findings into their protocols.
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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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".