Spatial evaluation of animal health care accessibility and veterinary shortage in France
Bibliographic record
Abstract
The decrease in the supply of veterinary healthcare in France adversely affects health of food-producing animals. In a One Health perspective, the health of people, animals and their shared environment are interconnected, and adequate supply of veterinary healthcare is required to ensure public health. Prevention of outbreaks and zoonotic diseases that may impact public health mobilizes a set of public policies, including strengthening veterinary workforce. These policies should be informed by quantification of animal health care accessibility, yet this has not been well characterized. The objective was to quantify the accessibility to veterinary healthcare for cattle, swine, and poultry sectors in France. A Two-Step Floating Catchment Area (2SFCA) approach was used to measure the level of accessibility to veterinary clinics. In the cattle sector, the 2SFCA score indicated relatively high accessibility in the north and south of France, but insufficient accessibility elsewhere. In the swine sector, there was high accessibility in the north east and in north of France, medium accessibility in the south west, and insufficient accessibility elsewhere. Finally, in the poultry sector, all regions had insufficient accessibility. Sensitivity analysis examining the effects of a change in spatial accessibility according to various travel time showed that the optimal threshold to compute 2SFCA score in cattle, swine and poultry sectors were respectively, 45, 60 and 60 min. According to a definition of "underserved area" derived from an official decree and the optimal thresholds to compute 2SFCA, the cattle, swine and poultry sector have on average 75.3, 89.9 and 98.3% underserved area, respectively. We provided evidence that the supply of animal health care was not sufficient and we proposed recommendations on how to assess animal health care accessibility, enabling modelling and visualization of the effects of potential public policies aimed at reducing veterinary shortages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".