The Effect of Human–Horse Interactions on Equine Behaviour, Physiology, and Welfare: A Scoping Review
Bibliographic record
Abstract
Human-horse interactions (HHIs) are diverse and prominent in the equine industry. Stakeholders have an invested interest in making sure that HHIs are humane. Assessment of equine welfare goes beyond physical health and includes assessment of the emotional state of the animal. HHIs can have a permanent effect on human-horse relationships, thereby influencing welfare. Therefore, an understanding of the horse's affective state during HHIs is necessary. A scoping review was conducted to: (1) map current practices related to the measurement of HHIs; (2) explore the known effects of HHIs on horse behaviour and physiology; and (3) clarify the connection between HHIs and equine welfare. A total of 45 articles were included in this review. Studies that used both physiological and behavioural measures of equine response to human interactions accounted for 42% of the included studies. A further 31% exclusively used physiological measures and 27% used behavioural observation. Current evidence of equine welfare during HHIs is minimal and largely based on the absence of a negative affective state during imposed interactions. Broadening the scope of methods to evaluate a positive affective state and standardization of methodology to assess these states would improve the overall understanding of the horse's welfare during HHIs.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".