Evaluating Communication Training at AVMA COE–Accredited Institutions and the Need to Consider Diversity within Simulated Client Pools
Bibliographic record
Abstract
The push for competency-based veterinary medical education by accrediting bodies has led to the inclusion of non-technical skills within curricula. Communication, self-awareness, and cultural humility are considered essential for post-graduate success. To facilitate skills development, veterinary educators have incorporated a variety of modalities including lecture, group discussions, virtual and peer-assisted learning, role play, video review of consultations, and simulated clients (SCs). The overarching goal is developing students into self-reflective practitioners through exposure to clinical scenarios that enhance and embody diversity. Decision making about case management is subject to stereotypes, bias, and assumptions. Racial and ethnic disparities reported in health care can adversely impact patient outcomes. This study was conducted to evaluate communication training and diversity among SC pools within veterinary colleges. A questionnaire was electronically disseminated to assistant/associate deans and/or directors of curriculum/education at 54 American Veterinary Medical Association Council on Education-accredited or provisionally accredited colleges of veterinary medicine. Twenty-one institutions are represented within the data set. Participating institutions summarized their communication curricula: 18 (85.71%) used SCs. Over 55% of these did not track SC demographic data or social identities; among institutions that did track, SCs were primarily monolingual English-speaking (77%), non-disabled (94.2%), white (90.4%), non-Hispanic/Latinx (98.6%) women (57%) over age 56 (64%). Sixteen institutions agreed with the statement "I do not feel that our SC pool is adequately diverse." Respondents shared that lack of time and capacity for recruitment were barriers to diversifying SC pools and proposed strategies to improve outreach.
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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 | 0.028 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".