‘First, do no harm’: systematic program evaluation of an equine veterinary service-learning initiative with Indigenous communities in Canada
Bibliographic record
Abstract
BACKGROUND: Veterinary students have historically lacked meaningful experiential learning opportunities in equine medicine. At the same time, there are barriers to accessing veterinary care in Indigenous communities stemming from colonial injustices. In 2018-2019, a partnership was initiated where University of Calgary students began to provide equine veterinary services to Indigenous communities. As the first-documented equine veterinary service-learning initiative in Indigenous communities embedded in a veterinary curriculum, the purpose of the study is to systematically evaluate the program for its potential impact as part of a formative process for improvement. METHODS: Multiple parties in the program were engaged in a convergent, parallel, mixed-methods systematic program evaluation to explore the main program outcomes: (1) equine veterinary care; (2) clinical experiential student education; (3) cultural training of veterinary professionals and students; and (4) education of community members. The hypothesis was that ethical development using the "first, do no harm principle" would lead to benefits including a healthy horse population, a technically and culturally competent veterinary community, and an educated horse clientele. RESULTS: The program had a positive impact on accessibility to veterinary care and self-reported improvement in veterinary and cultural competency. In addition to the hypothesized program outcomes, additional program outcomes and effects were identified, including reciprocal learning and relationship building with the Indigenous community, leading to trust and equity-building. The students learned from both the in-community programming as well as the Indigenous community members they worked with. CONCLUSION: Program evaluation of an equine service-learning initiative in Indigenous communities reveals multiple and profound impacts including improved patient health status, wider scope of veterinary and cultural learning, strengthened relationships, and reciprocal learning with partnering Indigenous communities.
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 | 0.068 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".