Reviewing the Undergraduate Veterinary Curriculum in Finland for Control Tasks in Veterinary Public Health
Bibliographic record
Abstract
To review and develop the undergraduate veterinary curriculum on official control in veterinary public health, an electronic survey was sent to 204 Finnish veterinarians employed in the field of food hygiene in 2005. The response rate was 44%. Most frequently cited as strengths of the current curriculum were extensive education and good knowledge. Respondents considered the main challenges in their work to be a wide field of activity, organizational changes, financial resources, organization of substitutes, and collaboration with decision makers. Of the 23 items to be included in the undergraduate curriculum, therefore, respondents prioritized state and local decision making, the role of the public servant, and leadership and management in the area of social factors; in the field of practical control work, in-house control systems, organizations and responsibilities, control techniques, and planning and targeting of controls were prioritized. Of areas traditionally covered in the undergraduate curriculum, legislation; legal proceedings and implications of controls; risks to human, animal, and plant health; and hazards in feed, animal, and food production were stated to be the most important. Although respondents were generally content with their career choice, veterinary public health tasks were not their first choice of career path immediately after graduation. Based on these findings, more attention should be focused on social aspects and practical training in official control in the undergraduate veterinary curriculum. The survey results also highlight the contrasts between society's needs and veterinarians' motivations and career-path expectations, which pose a significant challenge for future curricula.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 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) | 0.003 | 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".