Developing new Clinical Educational Partnerships:a shared experience
Bibliographic record
Abstract
Background: Bristol Veterinary School has a number of educational partnerships with a range of clinical providers, as part of a semi-distributed model of final year clinical teaching. Establishing a clinical educational partnership requires close collaboration between the partners, defining new ways of working and establishing effective processes and relationships. This workshop, led by representatives from both Bristol Veterinary School and CVS Ltd (one of Bristol Vet School’s clinical educational partners), will give participants an opportunity to consider the priorities and challenges inherent to establishing new clinical educational partnerships. Summary of work: Through discussion and sharing of experiences, participants will be invited to consider aspects such as relationship-building, training needs, assessment processes, logistical considerations and quality assurance. We will discuss “ways of working” at practice and institutional levels, and the needs and expectations of students, the university, the practices, and accrediting bodies. Take home message: The development of new clinical educational partnerships is a significant undertaking for both the University and the clinical provider. Participants should leave the workshop with insight into some of the key priorities and challenges that need to be considered when establishing new partnerships. Participants will have the opportunity to explore potential strategies for success from the perspective of both the University and a clinical educational partner.
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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 | 0.063 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 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".