Enhancing Primary Care Learning in a Referral Hospital Setting: Introducing Veterinary Clinical Demonstrators
Bibliographic record
Abstract
With the majority of veterinary graduates entering primary care practice (PCP), there is increasing recognition of the importance of preparing students to practice across a broad spectrum of care (SoC). The traditional model of veterinary training, focused on the referral hospital environment, can make this challenging. In 2018, Bristol Veterinary School recruited five primary care (PC) veterinary surgeons as veterinary clinical demonstrators (VCDs) who collaborated with rotation-specific specialists to help enhance student focus upon day-one skills and to emphasize SoC relevance of the referral caseload. To evaluate the initiative, two separate online surveys were disseminated to clinical staff and final year veterinary students. The survey was completed by 57 students and 42 staff members. Participants agreed that VCDs helped students feel prepared for a first job in primary care practice (students 94.7%; staff 92.7%); helped students to focus on the primary care relevance of referral cases (students 96.5%; staff 70.8%); helped students develop clinical reasoning skills (students 100%; staff 69.3%), practical skills (students 82.4%; staff 72.5%), and professional attributes (students 59.6%; staff 71.4%). Thematic analysis of free-text comments revealed the benefits and challenges associated with implementing the role. The data gathered helped to guide the role's ongoing development and to provide recommendations for others who may be looking to implement similar educational initiatives to help prepare graduates to practice across a spectrum of care.
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".