Knowledge, Skills, and Attitudes of Veterinary College Deans: AAVMC Survey of Deans in 2010
Bibliographic record
Abstract
The purposes of this Association of American Veterinary Medical Colleges (AAVMC) study was to develop a profile of deans to understand the knowledge, skills, and attitudes that current deans of schools and colleges of veterinary medicine consider important to job success and to inform the association's leadership development initiatives. Forty-two deans responded to an online leadership program needs survey, which found that knowledge, skills, and abilities related to communication, finance and budget management, negotiation, conflict management, public relations, and fundraising were recommended as the most important areas for fulfilling a deanship. Most respondents speculated that the greatest challenges for their institutions will be in the areas of faculty recruitment and retention and financing veterinary education. Reflecting on their experiences, respondents offered an abundance of advice to future deans, often citing the importance of preparation, communication, and leadership qualities as necessary for a successful and satisfying deanship. More than three-quarters of the respondents indicated moderate to high interest in an AAVMC multi-phase leadership training program to develop administrative leaders. A nearly equal number also indicated support for formal leadership training for current veterinary medical college and school deans. The study suggests leadership development topics that AAVMC could provide at existing meetings or through new programming. The study also suggests directions for individual institutions as they seek to implement leadership development activities at the local level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".