Exploring the Teaching Motivations, Satisfaction, and Challenges of Veterinary Preceptors: A Qualitative Study
Bibliographic record
Abstract
Optimization of clinical veterinary education requires an understanding of what compels veterinary preceptors in their role as clinical educators, what satisfaction they receive from the teaching experience, and what struggles they encounter while supervising students in private practice. We explored veterinary preceptors' teaching motivations, enjoyment, and challenges by undertaking a thematic content analysis of 97 questionnaires and 17 semi-structured telephone interviews. Preceptor motivations included intrinsic factors (obligation to the profession, maintenance of competence, satisfaction) and extrinsic factors (promotion of the veterinary field, recruitment). Veterinarians enjoyed observing the learner (motivation and enthusiasm, skill development) and engaging with the learner (sharing their passion for the profession, developing professional relationships). Challenges for veterinary preceptors included variability in learner interest and engagement, time management, and lack of guidance from the veterinary medicine program. We found dynamic interactions among the teaching motivations, enjoyment, and challenges for preceptors. Our findings suggest that in order to sustain the veterinary preceptor, there is a need to recognize the interplay between the incentives and disincentives for teaching, to foster the motivations and enjoyment for teaching, and to mitigate the challenges of teaching in community private practice.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".