Development of the Animal Management and Husbandry Online Placement Tool
Bibliographic record
Abstract
The workplace provides veterinary students with opportunities to develop a range of skills, making workplace learning an important part of veterinary education in many countries. Good preparation for work placements is vital to maximize learning; to this end, our group has developed a series of three computer-aided learning (CAL) packages to support students. The third of this series is the Animal Management and Husbandry Online Placement Tool (AMH OPT). Students need a sound knowledge of animal husbandry and the ability to handle the common domestic species. However, teaching these skills at university is not always practical and requires considerable resources. In the UK, the Royal College of Veterinary Surgeons (RCVS) requires students to complete 12 weeks of pre-clinical animal management and husbandry work placements or extramural studies (EMS). The aims are for students to improve their animal handling skills and awareness of husbandry systems, develop communication skills, and understand their future clients' needs. The AMH OPT is divided into several sections: Preparation, What to Expect, Working with People, Professionalism, Tips, and Frequently Asked Questions. Three stakeholder groups (university EMS coordinators, placement providers, and students) were consulted initially to guide the content and design and later to evaluate previews. Feedback from stakeholders was used in an iterative design process, resulting in a program that aims to facilitate student preparation, optimize the learning opportunities, and improve the experience for both students and placement providers. The CAL is available online and is open-access worldwide to support students during veterinary school.
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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 | 0.001 | 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) | 0.001 | 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".