Qualitative Analysis of Intern Applications and its Relationship to Performance
Bibliographic record
Abstract
This study aimed to identify qualitative aspects of small animal veterinary internship applications that are associated with relative intern performance. This study took place with data collected on small animal interns from the 2015-2016, 2016-2017, and 2017-2018 intern classes from four different institutions. Applicants were divided into top-performers and bottom-performers by sorting the calculated overall scores from highest to lowest, labeling the top half of interns as "top-performers," and the lower half of interns as "bottom-performers." Thematic analysis of the intern applications was conducted. Relationship skills and knowledge application were identified as themes in the top-performing interns but not in the bottom-performing interns. Veterinary experience, presentations, community service, research, and teaching were all seen more frequently in the top-performing interns. More top performers had characteristics of greatest strength of technical skills, professionalism, relationship skills, and teamwork. More bottom performers had characteristics of greatest strength of stress management, communication, and patient care. More top performers had characteristics that would benefit from targeted mentoring of leadership. More bottom-performers had characteristics that would benefit from targeted mentoring of technical skills, general knowledge, and self-awareness. In narrative comments, adaptability, and self-awareness were more commonly noted in the bottom-performers. Lack of confidence was noted as a theme in the bottom-performers, but not in the top-performers. Certain qualities of intern applications may be used to predict top- or bottom-performing interns.
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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 | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".