Active Rotation Management: Managing Clinical Veterinary Students in a Community-Based Teaching Model
Bibliographic record
Abstract
Distributed or community-based models for delivering clinical teaching to final-year veterinary students are becoming increasingly common. Managing real clinical problems in an authentic clinical environment drives students' intrinsic motivation and should prepare them more effectively for work in practice at graduation. These models do, however, present challenges, particularly around consistency of delivery and quality assurance management. The community-based model developed and refined at the University of Nottingham School of Veterinary Medicine and Science is different from those used elsewhere. Small groups of students spend 2-week blocks in the premises owned by third parties in which they complete their clinical training. In all core clinical teaching sites, the school places approximately one member of the clinical staff per group of students, but students are taught by both school staff and non-school staff employed by the third party. Over the first 13 years of delivering clinical teaching in this model, a process of "active rotation management" has evolved to ensure consistency of the student experience across all the sites used. The challenges and issues presented by this model has led to the development of a series of "dos" and "don'ts" that inform the success of the model. Based on surveys of how well the students and employers feel rotations have prepared them for practice, this model seems to represent an effective method of delivering appropriate clinical teaching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".