Cultural Competence Is Everyone’s Business: Embedding Cultural Competence in Curriculum Frameworks to Advance Veterinary Education
Bibliographic record
Abstract
Cultural competence in professional and research practice is important to effectively deliver animal and One Health services and programs. Veterinarians work with culturally and linguistically diverse teams, clients, and communities. Cultural perspectives on the significance and perceptions of animals and differences in consultation and engagement protocols and strategies can influence client–practitioner and researcher–community relationships, impacting animal health, welfare, and/or research outcomes. Curricula have been proposed to build cultural capacity in graduates, but these have not been reported in veterinary programs, and early attempts to integrate cultural competency into the University of Sydney veterinary curriculum lacked a formal structure and were ad hoc with respect to implementation. To address this, the authors introduced a broad curriculum framework into the University of Sydney veterinary program, which defines cultural competence, perceptions of animals, effective communication, and community engagement in a range of contexts. Cultural competency learning outcomes were described for units of study. These were contextually relevant and aligned to course learning outcomes and University of Sydney graduate qualities. Constructive alignment was achieved by linking learning outcomes to teaching and learning activities and assessment. The continuum of cultural competency underpinned mapping of cultural competency across the curriculum with staged, vertical integration of key principles. Additionally, action to engage staff, students, and stakeholders in a cultural competence agenda assisted in sustaining curriculum change. The result was integration of cultural competency across the curriculum aligning with recommendations from accrediting bodies and with best practice models in medicine, nursing, and allied health programs.
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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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".