A Qualitative Study of How On-Campus Faculty and Off-Campus Preceptors Evaluate Veterinary Students’ Professionalism
Bibliographic record
Abstract
Professionalism is defined and described in a variety of ways that differ considerably in details and quantity. While professionalism has become increasingly important, educators' opinions regarding the types of professionalism vary. The objective of this qualitative study was to evaluate faculty and preceptors' perspectives regarding veterinary medical students' professionalism during their clinical rotations. A thematic content analysis was performed to classify 2,014 comments. Five main themes emerged: (a) work ethic and attitude; (b) effective interactions with clients and delivering patient care; (c) effective interactions with health care professionals; (d) punctuality, task completion, and organization; and (e) commitment to improving competency in self and others. The importance of professionalism was stressed by both groups of faculty and preceptors through written comments; however, the magnitude of each theme differed. The results indicate that without understanding professionalism elements, the lack of conceptual clarity and consensus related to expected behaviors and attitudes would make it challenging to assess professionalism appropriately. The themes identified can be used to begin a discussion about expected behavior among faculty, preceptors, and students, therefore prompting a reasonable assessment of professionalism, as well as avoiding unprofessional behavior.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".