Development and Evaluation of a New Educational Resource for Farm-Health Planning
Bibliographic record
Abstract
Simple Templates for Everyday Planning and Support (STEPS) was created to illustrate the dynamic process of farm-health planning. Farm-health planning, also known as herd health or flock health planning, is a holistic, proactive management approach to animal health and welfare. This resource was used in a teaching seminar held for two groups of fourth-year veterinary students over a two-year period. Students answered a questionnaire before and after the teaching seminar that revealed that most participants believed the new resource had increased their knowledge of and ability to undertake farm-health planning. Students also completed one of four species-specific case examples that were evaluated by the first author. Most students (90.7%) included at least half of the essential farm-health planning factors in their case example submission. Twenty-six of these essential health planning factors were included by at least 80% of study participants. Six essential health planning factors received less than 20% of the student response rate. The traditional veterinary skills, which involve the management of individual animals, such as an assessment of the severity of lesions, were well represented in all of the case examples. However, the monitoring step of farm-health planning, such as the use of intervention levels, was the least well answered by the student population. In conclusion, the research study found that the STEPS seminar was successful in introducing many of the main principles of farm-health planning to two groups of fourth-year veterinary students.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".