A Qualitative Study of Why Students Choose to Study Veterinary Nursing
Bibliographic record
Abstract
Veterinary nursing (VN) is a popular subject among undergraduate students, but due to the high attrition rates from the profession there is a shortage of registered VNs. By identifying the factors that motivated student VNs to enroll in their degree program and persist to the final, it may be possible to enhance the support available for students when they are deciding whether to study VN. Online semi-structured interviews were used with 10 student participants from the final year of a BSc (hons) VN program. The data were analyzed using a six-step method of thematic analysis. The Situated Expectancy-Value Theory was used as a framework to interpret the results and allowed for an in-depth understanding of the participant's values and beliefs to be obtained. The results highlighted that a high intrinsic value for animals is a common reason for enrolling on the program, but that, partly due to the representation of the VN profession in marketing materials, at enrollment students do not seem to have a thorough understanding of the VN job role. As students' progress through their training journeys, they develop a sense of professional identity that motivates them to continue, but they also gain an insight into the challenging reality of the VN role. VN marketing materials need to be improved to ensure they provide prospective student VNs with an accurate insight into the realities of the VN job role. They will then be in a position to make an informed choice to join the VN profession.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".