The Effect of Human–Horse Interactions on Equine Behaviour, Physiology, and Welfare: A Scoping Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it