Perceptions of Veterinary Faculty Members Regarding Their Responsibility and Preparation to Teach Non-technical Competencies
Bibliographic record
Abstract
The development of non-technical competencies has become an important component of veterinary education. In this study, we determined faculty perspectives regarding their perceived involvement and ability in the cultivation of these competencies. A survey was administered to faculty members at five institutions. Respondents were asked whether the competency should be taught in their own courses and how prepared they felt to teach and evaluate the competency. Responses were analyzed by participant institution, gender, terminal degree and year, discipline, rank, and teaching experience. More than 90% of faculty respondents reported a personal responsibility to teach or cultivate critical thinking skills, communication skills, self-development skills, and ethical skills, with more than 85% also agreeing to a role in skills such as interpersonal skills, creativity, and self-management. The lowest percentages were seen for crisis and incident management (64%) and business skills (56%). Perceived preparedness to teach and evaluate these competencies paralleled the preceding findings, especially for the four consensus competencies and self-management. Faculty preparedness was lowest for business skills. Junior faculty were somewhat less likely than others to perceive a responsibility to teach non-technical competencies; however, instructors were more prepared to teach and evaluate business skills than were other faculty. Institutional trends were evident in faculty preparation. Although male faculty and non-DVM faculty tended to report a higher degree of preparedness, few differences reached statistical significance. Faculty perceptions of their responsibility to teach non-technical competencies vary by competency and parallel their perceived preparedness to teach and evaluate them.
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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 | 0.006 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".