‘It’s more emotionally based’: Prince Edward Island horse owner perspectives of horse weight management
Bibliographic record
Abstract
Horse obesity is a growing concern that can result in negative welfare. The role horse owners play in horse weight management is not well understood. This study aimed to: (1) explore the attitudes, beliefs, and perceptions of owners with overweight or obese horses regarding their horses' weight; and (2) understand the motivators and barriers for owners to implement, improve and maintain weight management-related strategies. A semi-structured interview guide based on the Theoretical Domains Framework was developed. Qualitative interviews were conducted with 24 owners in Prince Edward Island, Canada whose horse(s) were previously classified as overweight or obese by a veterinarian. Interviews were analysed using template analysis, organising patterns in the data into a codebook and overarching themes. Owners believed horse weight management was important, however, their perceived complexity of the issue made the implementation of the weight management practices difficult. Owners held conflicting perceptions, viewing overweight horses as well cared for, yet recognised these horses were at increased risk for negative health outcomes. Ultimately, participants felt emotionally torn about compromising their horse's mental well-being to address weight issues. Owners considered the practicality of weight-management strategies, the strategies' effectiveness, and whether recommended strategies aligned with their beliefs regarding good horse care practices. Knowledge was embedded into owners' understanding of horse weight, however, some highlighted that traditional knowledge dominates the equine industry hindering systemic industry change. Increased understanding of the effectiveness and impacts of weight management strategies on horses and fostering a society that recognises and accepts horses within a healthy weight range are warranted.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".