Bibliographic record
Abstract
OBJECTIVE: To define and describe the population of Australian veterinarians who work with horses. METHOD: Questionnaires were mailed to 866 veterinarians who had been identified as working with horses, and 87% were completed and returned. Data were entered onto an Excel spreadsheet, and analysed using the SAS System for Windows. RESULTS: About 12% of Australia's veterinarians were doing all the veterinary work with horses, and about 3% worked exclusively (> 90%) with horses, but did more than half (58%) of the horse work. Veterinarians working with horses included more males (80%) than the veterinary population as a whole (approximately 60%). Males had an average age of 47 years, females 35. Almost all (94%) worked in private practice, with 31% being employees, 28% partners and 41% sole owners. Females were more likely to be employees than males. Males reported working 55 hours/week; females 49. More females (44%) than males (16%) had worked less than full-time for more than a year. Males expected to work for another 12 years in full-time equivalents, and females for 16. One quarter (24%) saw only horses, but treated 58% of total horse cases. One-half had < 25% horses, and 29% had < 10% of horses in their caseloads. More of the older (54% of those aged > 60) than younger respondents (27% of those < 40) had grown up on farms with animals. One-quarter (24%) decided to become a veterinarian while in primary school, and females decided at a younger age than males. Overall, younger respondents decided at a younger age than did their older counterparts. A veterinarian contributed to the decision for 21% of these veterinarians. CONCLUSION: In this survey, Australian veterinarians who work with horses were found to be typically male, and advanced in their careers. As these older veterinarians retire, there may not be enough veterinarians who are committed to and competent with horses to take their places.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".