Bibliographic record
Abstract
OBJECTIVE: To analyse the number and distribution of the Australian veterinary work-force, and to assess changes in it that have occurred since 1981. PROCEDURE: The postcode and gender of each veterinarian who was registered, resident and apparently working in each state and territory in 1981, 1991 and 2001 was obtained from veterinary board lists, entered on an Excel data base, and analysed using the statistical program SAS System 7 for Windows 95. The Official Australian Postcode Map was used to determine the location of postcode areas, and publications of the Australian Bureau of Statistics provided data on human populations. RESULTS: At the end of June 2001, 6358 veterinarians were registered, resident and apparently working in a state or territory of Australia. This was 100% higher than in 1981. Over the intervening 20 years, the number had increased by 3181, 2001 of whom were female. Between 1981 and 1991 the increase in major cities was 898 and in rural areas 682, of whom 357 were male. Between 1991 and 2001 the increase in cities was 1139 and rural areas 462, of whom only 98 were male. The density of veterinarians in Australia - 330/million people - is higher than in the UK (213/million), USA (218/million) and Canada (250/million). CONCLUSIONS: The relative number of veterinarians in Australia is now higher than in the UK, USA and Canada, and is likely to continue to increase. There is evidence of maldistribution, with many rural practices facing shortages of veterinarians with the experience and inclination to maintain veterinary services over the longer term, and some cities likely to become overcrowded with veterinarians.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 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".