Veterinary Student Skills Learned at an Access to Care Clinic: Beyond Medicine and Surgery
Bibliographic record
Abstract
Incorporating curriculum to effectively help veterinary students learn how to provide accessible quality care to all pet owners is needed. The primary aims of this study are to explore how a 2-week rotation at a veterinary medical service-learning clinic (Wisconsin Companion Animal Resources, Education, and Social Services [WisCARES]) improves (a) comfort in working with clients from diverse race and low socioeconomic (SES) backgrounds and (2) confidence in leading cases, communication skills, and providing a spectrum of care options. Students were surveyed at five time points pre-rotation: mid-week 1, mid-week 2, end of rotation, and 1 month post. A total of 115 survey series were at least partially completed. Of the 97 responses that included background information, 68 (70%) students reported having “no to a few weeks” of experience working with diverse or low SES populations. When comparing themselves to before starting the rotation, student responses indicated increased comfort (mean = 4.54, standard deviation [ SD] = 0.54) and compassion (mean = 4.42, SD = 0.78) working with low-income or homeless populations, more comfort interacting with members of different race or ethnicity groups (mean = 4.21, SD = 0.82), and more appreciation for the human–animal bond (mean = 4.42, median = 5). Students also reported that spending time at WisCARES positively impacted their confidence in a clinical setting, managing and communicating about financial decisions, and approaching cases creatively. Giving students an opportunity to lead cases with clients from diverse races and low SES backgrounds can enhance levels of comfort with practice and improve confidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".