Establishing the Most Important Clinical Skills for New Graduate Veterinarians by Comparing Published Lists with Regional Stakeholder Expectations: A Bangladesh Experience
Bibliographic record
Abstract
Veterinary clinical skills training is evolving rapidly around the world and there is increasing interest in Bangladesh in opening clinical skills laboratories and using models in teaching. The first clinical skills laboratory was opened at Chattogram Veterinary and Animal Sciences University in 2019. The current study aimed to identify the most important clinical skills for veterinarians in Bangladesh to inform the further development of clinical skills laboratories and ensure resources are deployed effectively and efficiently. Lists of clinical skills were collated from the literature, national and international accreditation standards, and regional syllabi. The list was refined through local consultation, focused on farm and pet animals, and was disseminated via an online survey to veterinarians and final-year students who were asked to rate the level of importance of each skill for a new graduate. The survey was completed by 215 veterinarians and 115 students. A ranked list was generated with injection techniques, animal handling, clinical examination, and basic surgical skills among the most important. Some techniques requiring specific equipment and some advanced surgical procedures were considered less important. As a result of the study, the most important clinical skills for a new graduate in Bangladesh have been identified for the first time. The results will inform the development of models, the use of clinical skills laboratories, and the design of clinical skills courses for veterinary training. Our approach of drawing upon existing lists followed by local stakeholders consultation is recommended to others to ensure clinical skills teaching is regionally relevant.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".