The Development of a Novel Questionnaire Approach to the Investigation of Horse Training, Management, and Behaviour
Bibliographic record
Abstract
The Equine Behaviour Assessment and Research Questionnaire (E-BARQ) is a questionnaire instrument developed to obtain quantitative data on the domestic equine triad of training, management, and behaviour of horses. The E-BARQ was developed to identify how changes in training and management impact behaviour over time, to define normal behaviour in horses, and to discover how to improve rider safety and horse welfare, leading to ethical equitation. During the development of the E-BARQ, we also investigated how best to motivate stakeholders to engage with this citizen science project. The pilot version of the E-BARQ collected qualitative data on respondents' experience of the questionnaire. The pilot questionnaire was developed with the assistance of an international panel (with professional expertise in horse training, equitation science, veterinary science, equestrian coaching, welfare, animal behaviour, and elite-level riding), and was used to collect data on 1320 horses from approximately 1194 owner/caregiver respondents, with an option for respondents to provide free-text feedback. A Rotated Principal Component Analysis of the 218 behavioural, management, and training questionnaire items extracted a total of 65 rotated components. Thirty-six of the 65 rotated components demonstrated high internal reliability. Of the 218 questionnaire items, 43 items failed to reach the Rotated Principal Component Analysis criteria and were not included in the final version of the E-BARQ. Survey items that failed the Rotated Principal Component Analysis inclusion criteria were discarded if found to have a less than 85% response rate, or a variance of less than 1.3. Of those that survived the Rotated Principal Component Analysis, items were further assigned to horse temperament (17 rotated components), equitation (11 rotated components), and management and equipment (8 rotated components) groups. The feedback from respondents indicated the need for further items to be added to the questionnaire, resulting in a total of 214 items for the final E-BARQ survey. Many of these items were further grouped into question matrices, and the demographic items for horse and handler included, giving a final total of 97 questions on the E-BARQ questionnaire. These results provided content validity, showing that the questionnaire items were an acceptable representation of the entire horse training, management, and behavioural domain for the development of the final E-BARQ questionnaire.
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.
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".