Identifying the Barriers to Incorporating Reflective Practice into a Veterinary Curriculum
Bibliographic record
Abstract
A portfolio with good reflective content can play a large role in learning and setting up the lifelong learning practice required by veterinary surgeons in practice or in research. The aim of this project was to investigate students' experience with their reflective diaries within an electronic portfolio (e-portfolio). Focus groups were conducted with veterinary students at the University of Liverpool in years 1-4 to explore student perceptions of the e-portfolio, with an emphasis on reflection. Three themes emerged from the qualitative analysis: assessment, understanding the assignment (i.e., is it a useful and fair exercise?), and student well-being (i.e., stress, professional accountability, anxiety). Students had clear concerns about the assessment and did not see the relevance of the reflective diaries to their future career and learning. This has led the university's School of Veterinary Science to restructure the reflections on professional skills in the portfolio.
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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 | 0.077 | 0.244 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".