Attitudes, Experience, and Self-Confidence of Veterinary and Veterinary Nursing Students in Small Animal Dentistry: A Survey Study
Bibliographic record
Abstract
Dental issues are extremely common in dogs and cats, underscoring the importance of veterinary professionals’ knowledge in dentistry. Nevertheless, dental problems are currently often underdiagnosed and, consequently, undertreated. This study investigated the attitudes, experiences, and self-confidence of veterinary (V) and veterinary nursing (VN) students in their final 2 years of study in small animal dentistry. An online questionnaire was distributed, and responses were received from 61% of V students ( n = 94) and 41% of VN students ( n = 72). The majority of both V students (61%) and VN students (69%) expressed a desire for more education in small animal dentistry. Furthermore, a minority of V students (20%) and VN students (22%) felt adequately prepared for their first day in practice after graduation. Less than half of the students (V 44% and VN 38%) had participated in a practical dental procedure outside training sessions. Self-confidence in small animal dentistry procedures was rated on a 0–10 scale. V students exhibited the highest confidence in teeth polishing (6.1) and removing tartar with ultrasonic scalers (6.0), while VN students were most confident in recognizing common oral/dental problems (6.0) and discussing dental issues with pet owners (5.3). Extra practical training significantly increased confidence in several dental procedures ( p < .005). Despite positive attitudes, a notable proportion of V and VN students feel unprepared for their first day in practice, potentially stemming from insufficient training. Addressing these gaps through clear guidelines for Day One Competence and enhanced practical training is crucial, ultimately benefiting the well-being of small animals.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".