Examination of Factors That Influence Students' Average Client Transactions in a Small-Animal Primary Care Clinical Environment
Bibliographic record
Abstract
The purpose of this study was to describe the average client transaction (ACT) of fourth-year veterinary students in a university community practice setting at the University of Georgia (UGA) and to investigate variables that may affect the students' ACT. The revenue generated by each student was assessed to determine whether gender, ethnicity, academic class rank, area of emphasis, and UGA versus non-UGA student could affect the ACT of the students. Two hundred one students were evaluated over 19 continuous 3-week-long clinical rotations. For all students, the M±SD gross revenue was $2,836±$1,051, the total number of client transactions was 18±6, and the ACT was $154±$35 per student. During the study, hospital fees (price class) increased four times. No student-related factors were significantly associated with the ACT in the univariate analyses. No factors except price class were found to be significant in the two-factor analyses. Generating an ACT equivalent to the national average demonstrates that the typical student at the community practice clinic should provide a level of productivity to the practice owners who hire these students. The factors measured demonstrated little influence on the student's revenue-generating ability at the community practice clinic. Mentorship provided to students for each appointment might have affected the study outcome. Other variables, such as communication style, may affect the ACT more than those investigated in this study and warrant further study.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".