Support for Neurodivergent Students in Veterinary Education Part 1: Current Practice and Roundtable Discussion of Recommendations
Bibliographic record
Abstract
Approximately 10% of undergraduate student populations are neurodivergent. Such students have differences in executive functioning and social communication skills, which can confer both strengths and challenges in the academic environment. Specific challenges presenting in the veterinary curriculum include the intense workload, unpredictable nature of work, and high level of interpersonal and communication skills required in clinical settings. Extramural studies (EMS) occur remote from university support systems, adding further challenge for some students. A survey was sent to all current United Kingdom veterinary schools in 2022 to identify current support for neurodivergent students. An interactive roundtable discussion was held to brainstorm best practice for harnessing the power of neurodiversity in the clinical learning environment. Several consistent themes emerged. Most veterinary schools provide some degree of support for neurodivergent individuals, but support varies widely. Four of the eight schools provide support at open days and/or following offers, with one school offering a summer school. Five schools confirmed that accommodations were made to clinical rotations and/or EMS in line with a support plan from their Disability Service. Despite these steps to assist neurodivergent students, support could, and should, be increased to improve the student experience. Suggested enhancements include a supportive environment for the empowerment of disclosure, neurodiversity awareness training for university staff and placement providers, provision of reasonable adjustment guidelines for EMS providers, clinical/intramural rotation orientation and support, and student access to a neurodiversity mentor/coach.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".