Medication compliance by cat owners prescribed treatment for home administration
Bibliographic record
Abstract
BACKGROUND: Most veterinary literature examining medication compliance has described the phenomenon in dogs. The evidence available regarding factors affecting cat owner medication compliance is limited. OBJECTIVES: Identify and describe factors associated with cat owners' noncompliance with veterinary recommendations for pet medications, as well as client-reported barriers and aids to administering medications prescribed by primary care veterinarians. SUBJECTS: Cat owners presenting their animals for veterinary examination and treatment. METHODS: A cross-sectional survey of cat owners' compliance with veterinary medication recommendations was performed from January 9, 2019, to July 18, 2020. A convenience sample of owners prescribed medication for their pets by veterinarians during or after elective veterinary examination was recruited to respond to questions regarding medication administration experience and compliance. Follow-up was obtained from owners to determine if the course of medication had been completed. Compliance data were analyzed descriptively, and logistic regression was performed. RESULTS: Medication noncompliance was recorded for 39% (26/66) of cat owners. A quarter (16/66) reported challenges in administering medication to their pets; the most commonly cited reason was a resistant pet. Oral administration of antibiotics was significantly associated with noncompliance (P = .01). Clients with limited pet ownership experience were less likely to be noncompliant (P = .04). CONCLUSIONS AND CLINICAL IMPORTANCE: Clients' inability to medicate their cats PO may have implications for clinical outcomes and antimicrobial stewardship. Alternatives to direct PO administration of solid-form medications in cats should be considered. Demonstrating administration techniques to all clients may improve compliance and influence clinical outcome.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".