Corporate Influence and Conflicts of Interest: Assessment of Veterinary Medical Curricular Changes and Student Perceptions
Bibliographic record
Abstract
The ethics document of the Association of American Veterinary Medical Colleges provides guiding principles for veterinary schools to develop conflict of interest policies. These policies regulate faculty and student interactions with industry, potentially reducing the influence companies have on students' perceptions and future prescribing practices. This paper examines the implementation of a conflict of interest policy and related instructional activities at one veterinary college in the US. To inform policy and curricular development, survey data were collected regarding veterinary students' attitudes toward pharmaceutical marketing, including their perceptions of their own susceptibility to bias in therapeutic decisions. Responses from this group of students later served as control data for assessing the effectiveness of educational programs in the content area. A conflict of interest policy was then implemented and presented to subsequent classes of entering students. Classroom instruction and relevant readings were provided on ethics, ethical decision making, corporate influences, and the issue of corporate influence in medical student training. Within seven days of completing a learning program on conflict of interest issues, another cohort of veterinary students (the treatment group) were administered the same survey that had been administered to the control group. When compared with the control group who received no instruction, survey results for the treatment group showed moderate shifts in opinion, with more students questioning the practice of industry-sponsored events and use of corporate funds to reduce tuition. However, many veterinary students in the treatment group still reported they would not be personally influenced by corporate gifts.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".