Development and Implementation of a Veterinary Spectrum of Care Clinical Rotation Aligned with the CBVE Model
Bibliographic record
Abstract
Spectrum of care (SOC) has recently been described in the literature, yet it is not an entirely new concept within the veterinary profession. Practitioners in general veterinary practice have long needed to provide a broad range of unique care options for their patients and clients, particularly those for whom referral is not possible. More recently, graduates and their employers have reported that new veterinarians often lack the competence and confidence to provide a broad array of care options, while training in ever more specialized tertiary-referral environments. To better prepare veterinary learners to cope with the variable nature of general veterinary practice and to better meet employer demands, The Ohio State University College of Veterinary Medicine purposefully backward designed learning experiences in a new outcomes-based curriculum so that SOC is emphasized and aligned with the foundation offered by the Competency-Based Veterinary Education (CBVE) model. A unique set of subcompetencies and educational goals were collaboratively developed and used to define a new final year rotation, with additional input provided by an advisory panel of practicing SOC veterinarians from private practice. Ideal caseload characteristics, case numbers, appointment length, daily activities, and other elements were defined, and final year student performance was monitored during implementation to assess progress in meeting key developmental milestones. Incorporating spectrum of care training at The Ohio State University shows promise for developing confidence and competence in new graduates, while also increasing their skills, and perhaps improving their mental health.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".