Comparison of Attitudes between Generation X and Baby Boomer Veterinary Faculty and Residents
Bibliographic record
Abstract
Understanding the characteristics and preferences of the different generations in the veterinary workforce is important if we are to help optimize current and future veterinary schools and teaching hospitals. The purpose of this study was to compare the attitudes of different generations of veterinary faculty and those of faculty and house officers. A survey administered to faculty and house officers asked respondents to identify their level of agreement with a series of statements addressing work and lifestyle issues and feedback preferences. In addition, the survey included an open-ended question on non-monetary rewards for hard work. Thirty-eight of 48 faculty members (79%) and 45 of 54 house officers (83%) completed the survey. Among faculty, there were no significant differences between the Generation X and Baby Boomer subgroups or between genders. More faculty than house officers responded that delayed gratification is acceptable (p = 0.03) and that it is difficult to balance home and work life (p < 0.001). Compared to faculty, house officers preferred more frequent (p = 0.03) and critical (p = 0.02) feedback. The most common responses to the question on effective non-monetary rewards for hard work, from both faculty and house officers, were recognition and time off. No attitudinal differences were detected between generations within the faculty group, but a number of significant differences emerged between faculty and house officers. Increased awareness of the importance of balance and rewards for hard work, as well as modification of feedback styles, may be beneficial in teaching and mentoring current and future generations.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".