Perceptions of the Veterinary Profession among Human Health Care Students before an Inter-Professional Education Course at Midwestern University
Bibliographic record
Abstract
Conflicts among health care professionals often stem from misperceptions about each profession's role in the health care industry. These divisive tendencies impede progress in multidisciplinary collaborations to improve human, animal, and environmental health. Inter-professional education (IPE) may repair rifts between health care professions by encouraging students to share their professional identities with colleagues in unrelated health care disciplines. An online survey was conducted at Midwestern University (MWU) to identify baseline perceptions about veterinary medicine among entry-level human health care students before their enrollment in an inter-professional course. Participation was anonymous and voluntary. The survey included Likert-type scales and free-text questions. Survey participants expressed their interest in and respect for the discipline of veterinary medicine, but indicated that their unfamiliarity with the profession hindered their ability to collaborate. Twenty percent of human health care students did not know the length of a Doctor of Veterinary Medicine (DVM) program and 27.6% were unaware that veterinarians could specialize. Although 83.2% of participants agreed that maintaining the human-animal bond is a central role of the veterinary profession, veterinary contributions to stem cell research, food and water safety, public health, environmental conservation, and the military were infrequently recognized. If IPE is to successfully pave the way for multidisciplinary collaboration, it needs to address these gaps in knowledge and broaden the definition of veterinary practice for future human health care providers.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".