Veterinary Students as Elite Performers: Preliminary Insights
Bibliographic record
Abstract
The KPMG ''Mega Study'' (Brown JP, Silverman JD. The current and future market for veterinarians and veterinary medical services in the United States. J Am Vet Med Assoc 215:161-183, 1999) and other studies (Cron WL, Slocum JV, Goodnight DB, Volk JO. Impact of management practices and business behaviors on small animal veterinarians' incomes. J Am Vet Med Assoc 217:332-338, 1999; Lewis RE. Non-technical Competencies Underlying Career Success as a Veterinarian: A New Model for Selecting and Training Veterinary Students. Minneapolis: Personnel Decisions, 2002) concur that improvement in veterinary practitioner performance is necessary. Improvement in practitioners' non-technical competencies is considered most vital. Little research exists that identifies underlying psychological factors harbored by veterinary students that inhibit ability to achieve sustained maximum professional performance. Left unaddressed, these same characteristics may lead to coping behaviors that disrupt or, in the worst cases, lead to voluntary or involuntary termination of professional careers. Several performance-related characteristics and interpersonal dynamics are investigated in this study that provide preliminary evidence for the long-term shortcomings addressed in previous veterinary practice management literature. Pedagogical recommendations for addressing these student psychological characteristics are submitted for consideration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".