Development of International Learning Outcomes for Shelter Medicine in Veterinary Education: A Delphi Approach
Bibliographic record
Abstract
Shelter medicine is a veterinary discipline of growing importance. Formally accepted as a clinical specialty in the US in 2014, the practice of shelter medicine worldwide is expanding. As a topic in veterinary pre-registration (undergraduate) education, it is frequently used as an opportunity to teach primary care skills, but increasingly recognized as a subject worthy of teaching in its own right. The aim of this study was to use a Delphi consensus methodology to identify learning outcomes relevant to shelter medicine education. Shelter medicine educators worldwide in a variety of settings, including universities, non-governmental organizations and shelters were invited to participate. Participants were initially invited to share shelter medicine teaching materials. These were synthesized and formatted into Learning Outcomes (LOs) based on Bloom's taxonomy and organized into five subject-specific domains. Participants were then asked to develop and evaluate the identified LOs in two rounds of online surveys. Consensus was determined at > 80% of panelists selecting "agree" or "strongly agree" in response to the statement "please indicate whether you would advise that it should be included in a shelter medicine education program" for each LO. In the second survey, where re-wording of accepted LOs was suggested, preference was determined at > 50% agreement. Through this method, 102 agreed LOs have been identified and refined. These LOs, as well as those which did not reach consensus, are presented here. These are intended for use by shelter medicine educators worldwide, to enable and encourage the further development of this important veterinary discipline.
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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 | 0.162 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".