Incomes and satisfaction among bovine focused veterinary practitioners in the United States and Canada
Bibliographic record
Abstract
Veterinarians with income from bovine-focused work were invited to complete an online survey about their 2021 income, employment and demographic characteristics and their levels of satisfaction with their job and their compensation. Survey responses were solicited by the American Association of Bovine Practitioners through a newsletter and promotion to conference attendees, and by the investigators though a bovine veterinarian group on Facebook. Our objectives were to improve pay transparency for bovine-focused veterinarians and to examine how incomes and satisfaction differed based on employment and demographic characteristics. We received 623 responses from veterinarians in partly or entirely bovine-focused practice, of which 414 responses were included in analyses and reporting. Factors that are positively associated with increased income include years since graduation, practice ownership, type-exclusive practice (beef or dairy only), and a production-based compensation structure. Income transparency is positively associated with job satisfaction, while being on call and working increased hours are negatively associated with job satisfaction. Reported incomes may have been somewhat depressed due to the reporting period falling during the COVID pandemic. This information about incomes and satisfaction among bovine practitioners may encourage increased income transparency and the use of compensation structures based entirely or in part on production.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".