Bibliographic record
Abstract
Abstract The field of equine sports medicine and rehabilitation is rapidly expanding with increasing career opportunities for individuals (non-veterinarians) in equine rehabilitation facilities. However, throughout the United States, there are limited educational opportunities for undergraduate students to prepare for this career. The objective of this study was to determine what practical skills and theoretical knowledge are deemed most useful for employment by professionals in this industry. To meet this objective, an Qualtrics survey was distributed through email and social media to veterinarians, veterinary professionals, rehabilitation service providers, and horse owners. In addition to demographics, the survey asked respondents to list practical skills and theoretical knowledge that are essential for professionals in the equine rehabilitation industry. The majority of the 117 respondents (84%) were located in the United States, with the remainder from Canada (5%), the United Kingdom (5%) and several other countries. Eighteen percent of respondents were veterinarians, 26% owned or managed rehabilitation facilities, 8.5% were veterinary technicians, and the remainder were horse owners, rehabilitation service providers, and others. Horse handling skills (19%) and communication skills (18%) were the most commonly listed practical skills deemed essential for rehabilitation professionals. Of the theoretical skills, evaluation of lameness (29.5%), anatomy (31%), and fundamentals of equine reconditioning programs (32%) were deemed similarly important for rehabilitation professionals. Together, these data indicate that an educational program in equine sports rehabilitation should include fundamental knowledge in lameness evaluation and rehabilitation methods as well as significant hands-on opportunities with rehabilitating horses and communicating about rehabilitation methods and progress with clients.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".