A Human Factors and Systems-Thinking Approach to Veterinary Patient Safety Education: Why, What, and How?
Bibliographic record
Abstract
Preparing graduates to practice safely in today's increasingly complex veterinary workplaces is a key aim for veterinary educators. This requires embedding veterinary patient safety concepts into already full curricula. This teaching tip explores the benefits of incorporating human factors and systems-thinking principles into the design and delivery of veterinary patient safety education, showing how these can inform both what is taught and how. We explore what taking a human factors approach to veterinary patient safety education means, inviting educators to rethink not just curricular content but the whole approach to delivery. Advocating for the adoption of a systems-thinking-informed approach to curriculum design, we present a two-stage curriculum mapping process to support educators to embed core human factors principles as a way of thinking and doing for learners and faculty alike. Learning theories and educational design that align with human factors principles promote participatory methods and encourage collaborative experiential learning, critical thinking, and authentic application of knowledge and skills. Educators should explore opportunities presented by interprofessional education and workplace-based learning for practical application of these principles. Barriers to an integrated human factors-based patient safety curriculum include inconsistent terminology and understanding, significant faculty development requirements, and assessment challenges associated with existing regulatory and licensing requirements. Practical approaches to addressing these barriers are discussed. The recommendations outlined for the design and delivery of veterinary patient safety curricula will help ensure that institutions develop graduates that are effectively prepared for the complexity they will meet in the veterinary workplace, leading to improved patient safety and overall workplace well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".