Impact of a Spectrum of Care Elective Course on Third-Year Veterinary Students’ Self-Reported Knowledge, Attitudes, and Competencies
Bibliographic record
Abstract
Veterinary services’ rising cost is an increasing barrier to pet care. Spectrum of care (SpOC) refers to evidence-based veterinary medicine options along the socioeconomic spectrum. To meet growing pet owner financial constraints and pet care needs, training to equip veterinarians with competencies to provide SpOC as Day One graduates is argued to be added as part of the veterinary curriculum. Objectives of our prospective pre- and post-survey study were to (a) determine baseline self-reported knowledge, attitudes, and competencies (KACs) surrounding SpOC in third-year DVM students; (b) develop and assess impact of a SpOC course on student self-reported SpOC KACs; and (c) obtain student feedback on the course and future SpOC training. Enrolled students ( n = 35) completed the pre-survey ( n = 35) and post-survey ( n = 33). Results indicated that students were aware of the need for SpOC training within the veterinary curriculum, and positive changes occurred in self-reported KACs from pre- to post-survey. Students tended ( p = .08) to predict better outcomes in SpOC cost-barrier scenarios from pre- (34%) to post-survey (76%), such as reduced perceived likelihood of euthanasia (63%–39%) and unsuccessful outcomes (40%–27%). Most students (31/33, 94%) predicted the course would benefit them in clinical practice and had preferred future training preferences (online modules [70%], seminars [60%], webinars [58%]). Data indicate benefits in student self-reported KACs following the SpOC course, warranting formal course inclusion, with tracking of students into clinical practice to document objective KAC impacts and perhaps similar course rollout to other institutions.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".