The Use of Psychoactive Medications and Non-medication Alternatives for Cats and Dogs in North American Animal Shelters and Rescues
Bibliographic record
Abstract
Introduction: Animals in shelters commonly experience fear, anxiety, and stress. Psychoactive medications and non-medication alternative options are recommended and used in shelters to manage these negative affective states, yet little is known about the current practices, perceptions, and barriers surrounding their use. Methods: This study investigated the use and perceptions of psychoactive medications and non-medication alternative options in animal shelters and rescues across Canada, the United States, and Mexico. An online survey collected responses from 233 participants representing 148 unique shelters, focusing on the frequency of use, comfort administering, and efficacy, as well as barriers and considerations influencing psychoactive medication and non-medication alternative use decisions. Results: Results showed that dogs experiencing fear were more frequently treated with psychoactive medications than cats experiencing fear. Gabapentin and fluoxetine were the most commonly used medications for fearful cats, while trazodone and gabapentin were the most commonly used for fearful dogs, with respondents reporting high ratings for both comfort in administering these medications to animals, and perceived efficacy of these medications at reducing anxiety in animals. Pheromones and nutraceuticals were the most commonly used non-medication alternative options. There was high agreement with the statement that admittance to an animal shelter is the main cause of behavior and welfare problems, and that welfare concerns are a key justification for providing psychoactive medication. Cost and uncertainty about the efficacy of treatments emerged as leading barriers to psychoactive medication administration. Specific characteristics, including the respondent’s role and type of shelter, influenced perceptions about psychoactive medications and alternatives, and barriers to their use. Conclusion: These findings highlight the importance of further understanding the efficacy and safety of psychoactive medication and alternatives, as well as addressing the barriers to their administration, to improve overall welfare outcomes for cats and dogs in shelter and rescue environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".