Determinants of access to animal health care in France: evidence from a spatial econometric framework
Bibliographic record
Abstract
Abstract Over the last two decades, concerns have arisen in the veterinary profession about the declining number of food animal veterinarians. Based on a One Health perspective which recognizes that the health of people, animals, and their environment are interconnected, the French policymakers implemented a set of policies to combat the veterinarian shortage in the food animal sector that may cause public health crises. However, public interventions are unlikely to succeed in combating the veterinarian shortage unless they are preceded by a relevant understanding of the main determinants underlying this shortage. This paper contributes to identifying the main factors of the veterinarian shortage in 2019 in the French cattle sector using databases that integrate French veterinary clinics, farm characteristics and socio-economics features, and a spatial econometrics framework. Our results highlighted, first, strong and positive spatial autocorrelation in terms of veterinarian shortage between observations. Second, favorable socio-economic characteristics of a region were associated with a reduction in veterinarian shortage. Third, proximity to urban regions was associated with a decreased veterinarian shortage. Based on these findings, we provided some recommendations to policymakers.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".