Does Studying Veterinary Medicine Improve Students’ Haptic Perception Ability? A Pilot Study With Two Age-Groups
Bibliographic record
Abstract
Haptic perception is an important tool for veterinarians. The present study analyzed the association between the haptic perception threshold of veterinary students and their palpatory experience. To approach this goal, 35 female students of veterinary medicine were divided into two groups with different levels of experience: (a) students with little practical experience, at the beginning of their studies (first year), and (b) students close to the end of their theoretical training (fourth year). To thoroughly evaluate the students' sense of touch, three different test procedures were used: the Haptic Threshold Test (HTT), the Haptic Figures Test (HFT), and tactile acuity. Contrary to our expectations, we found worse mean haptic perception thresholds (HTT) in the more experienced students than in the less experienced group. This effect was significantly correlated with age. Furthermore, we found that longer exploration times were not sufficient to compensate for shortcomings in haptic perception. We also found large interindividual differences. Future studies should investigate whether and to what extend these effects have an impact on students' palpation performance on simulators and live animals. Moreover, which beneficial effects may be achieved through an additional haptic training for students with inferior haptic thresholds should be investigated. Improving haptic perception abilities in veterinary students could be one important step toward achieving satisfactory Day One Competences in university graduates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".