Supporting Veterinary Preceptors in a Distributed Model of Education: A Faculty Development Needs Assessment
Bibliographic record
Abstract
Effective faculty development for veterinary preceptors requires knowledge about their learning needs and delivery preferences. Veterinary preceptors at community practice locations in Alberta, Canada, were surveyed to determine their confidence in teaching ability and interest in nine faculty development topics. The study included 101 veterinarians (48.5% female). Of these, 43 (42.6%) practiced veterinary medicine in a rural location and 54 (53.5%) worked in mixed-animal or food-animal practice. Participants reported they were more likely to attend an in-person faculty development event than to participate in an online presentation. The likelihood of attending an in-person event differed with the demographics of the respondent. Teaching clinical reasoning, assessing student performance, engaging and motivating students, and providing constructive feedback were topics in which preceptors had great interest and high confidence. Preceptors were least confident in the areas of student learning styles, balancing clinical workload with teaching, and resolving conflict involving the student. Disparities between preceptors' interest and confidence in faculty development topics exist, in that topics with the lowest confidence scores were not rated as those of greatest interest. While the content and format of clinical teaching faculty development events should be informed by the interests of preceptors, consideration of preceptors' confidence in teaching ability may be warranted when developing a faculty development curriculum.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".