A Unique Spectrum of Care Tool Provides a Self-Regulated Learning Opportunity and Facilitates Client Communication
Bibliographic record
Abstract
Providing only the most state-of-the-art, intensive, and expensive level of treatment available does not meet the needs of every pet owner and pet. To overcome this barrier, veterinarians are working to provide spectrum of care (SOC) options to pet owners. This teaching tip describes the creation of a Spectrum of Care Options Presentation and Explanation (SCOPE), a tool that can serve a variety of educational purposes and improve delivery of care across the spectrum of care. The SCOPE considers andragogy, evidence-based medicine, and pet owner preferences related to communication as well as the cost of care. The use of a SCOPE during oncology consultations led by an intern on an oncology service rotation demonstrated its utility in identifying evidence-based SOC options for pets with cancer, serve as a self-regulated learning experience for the participating intern, and elicit pet owner and pet contextual issues impacting the care plan. The SCOPE can be used to promote the implementation of SOC in veterinary medicine. The SCOPE is flexible and may be adapted for use in disciplines other than oncology and with a variety of learners, such as veterinary students, or in early career mentoring 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".