Preparing Faculty for the Future: AAVMC Members' Perceptions of Professional Development Needs
Bibliographic record
Abstract
Our purpose in this study was to determine professional development needs of faculty in the Association of American Veterinary Medical Colleges' (AAVMC's) member institutions, including those needs associated with current and emerging issues and leadership development. The survey asked respondents to report their level of job satisfaction and their perceptions of professional development as they related to support and resources, teaching, research, career planning, and administration. Five hundred and sixty-five individuals from 49 member institutions responded to an online professional development needs survey. We found that job satisfaction was associated with a variety of workplace variables correlated with academic rank, with those of higher academic rank expressing greater levels of satisfaction. Respondents with tenure also expressed generally higher levels of satisfaction. Most of the respondents expressed interest in learning more about topics related to teaching (e.g., effective questioning, giving feedback, principles of learning and motivation), research (e.g., research design, writing grants), career planning (e.g., mentoring, time management), and administration (e.g., fostering innovation, enhancing productivity, improving the work environment). Just more than half of the respondents indicated moderate to high interest in an AAVMC multi-phase leadership training program. The study suggests topics for which AAVMC should provide professional development opportunities either at existing meetings or through new programming. The study also suggests directions for individual institutions as they seek to implement professional development activities at the local level.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".