Exploring the Effects of Participation in a Shelter Medicine Externship on Student Knowledge and Self-Confidence
Bibliographic record
Abstract
With the recognition of shelter medicine as a sub-discipline of veterinary medicine, many veterinary programs are including clinical shelter experiences in their curricula. A concurrent mixed-methods study was designed with the aim to determine the effectiveness of a 4-week clinical shelter medicine program on students' perception of canine and feline surgical proficiency and shelter medicine knowledge as well as gain information on student attitudes toward shelter medicine and interest in pursuing this career path. Year 4 veterinary students at Western University of Health Sciences who were enrolled in a shelter medicine externship from 2014 to 2016 were invited to complete pre- and post-course online surveys. Of the 168 enrolled students, 77.4% were invited to participate, and 114 completed the survey. There was no statistically significant difference in student veterinary career interest before and after completing the externship. There was an overall increase in student rating of their ability to perform six shelter medicine tasks as well as ovariohysterectomy/ovariectomy (OVH/OE) and castration surgeries after completing their externship; all were statistically significant ( p < .001). There was a statistically significant difference in the proportion of students who were familiar with the Asilomar Accords and the Association of Shelter Veterinarians (ASV) Guidelines for Standards of Care in Animal Shelters after completion of their externship ( p < .001). Students largely supported pediatric spay and neuter, stating it was an effective means of population control. Based on the themes that emerged from the qualitative analysis, it appeared most students had an accurate understanding of the term no-kill.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".