Talking treats: A qualitative study to understand the importance of treats in the pet-caregiver relationship
Bibliographic record
Abstract
Treats are a prevalent aspect of pet care, frequently given by dog and cat caregivers for varying reasons. However, recommendations of reducing or eliminating treat feeding poses a common challenge, leading to potential non-adherence surrounding weight management practices. To explore caregivers' perceptions and experiences surrounding treat feeding, we conducted five online focus groups with 24 dog and cat caregivers, recruited via an infographic shared on social media using snowball sampling. NVivo12© was used to organize and analyze verbatim transcripts using inductive thematic analysis. Outcomes illustrated three major themes: 1) the role of treats as an important tool for caregivers; 2) considerations for treat selection and provision; and 3) caregivers' need for more and better information and support related to treats. Participants emphasized the importance of treats for managing behaviours, health-related activities, and enhancing the pet-caregiver relationship. Results suggest that the diverse and valued applications of treats, caregivers' satisfaction associated with treat-giving, and perceived lack of guidance surrounding treats may present challenges for caregivers in reducing treat feeding with their pets. Findings highlight opportunities to enhance the available resources that can empower both veterinary professionals and caregivers to make well-informed decisions and foster sustainable changes in treat feeding practices to support weight management and overall health. Such considerations can improve client compliance with veterinary recommendations, to promote companion animal health and well-being while fostering the human-animal bond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".