Perceptions of the Human–Animal Bond in Veterinary Education of Veterinarians in Washington State: Structured versus Experiential Learning
Bibliographic record
Abstract
RATIONALE FOR THE STUDY: The purpose of this research was to describe Washington private practitioners' beliefs about how the Human-Animal Bond (HAB) should be addressed in DVM curricula and continuing education. METHODOLOGY: 1,602 Washington veterinarians in private practice were asked to participate in an online survey that addressed the importance of HAB and of DVM/post-DVM HAB education. RESULTS AND CONCLUSIONS: The response rate was 25.9% (415/1,602). Eighty-one percent (334/412) of respondents indicated that the HAB was important to their decisions to become veterinarians. The HAB was more important to the most recent graduates than to earliest graduates and to females than to males. Forty-four percent (184/415) of respondents considered mentoring to be the best way to learn about the HAB while in veterinary school. Of the 40% (165/415) of respondents who indicated that their veterinary schools offered structured learning on the HAB, 89% (145/163) said they had participated in it. Seven percent (29/415) indicated that entry-level veterinarians were very prepared to identify and facilitate the HAB, while 54% (224/415) said that they were somewhat prepared. Only 32% (131/415) had participated in any HAB structured learning since having started practicing, and the earliest graduates were twice as likely to have participated as the most recent graduates. More than half (55%, 223/407) disagreed or strongly disagreed that post-DVM, the best way to learn about the HAB is through structured learning. However, 83% (342/414) agreed that continuing-education credits should be given for HAB classes. Eighty-six percent (358/414) supported additional HAB research. Ninety-seven percent (402/414) agreed that the best way to learn about the HAB is through experience. These results suggest that veterinarians do not value HAB structured learning as much as experiential learning and that they are not very confident in recent graduates' abilities regarding the HAB. We propose HAB structured learning in the first three years of DVM education, complemented by the incorporation of the HAB into clinical rotations and post-DVM continuing education.
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 | 0.010 | 0.016 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".