Frequency of Interactions Between Veterinarians and Other Professionals to Guide Interprofessional Education
Bibliographic record
Abstract
The objective of this study was to determine the frequency with which veterinarians interact with individuals in other health professions and related fields of work. Four thousand members of the American Veterinary Medical Association (AVMA) were surveyed. Demographic information was collected, and participants were asked how frequently they interacted with members of various professions. General comments were solicited regarding which professions veterinarians should reach out to in order to enhance veterinary health care and which professions veterinarians could interact with to improve human health care. The overall response rate was 18.6%. Demographic information suggested that the sample was representative of the profession. Frequency of interactions ranged from never to regularly, but for no group or profession was frequent interaction the norm. For practitioners, interactions with pharmacists, both dispensing and compounding, and with drug company representatives were most common. For educators, interaction with pharmacists and drug company representatives was also high compared to other professions, and they were also more likely to interact with laboratory technicians and physicians. For those in industry, interactions with laboratory technicians, physicians, drug and food company representatives, and public health officials were most common. All other respondents described their most common interactions as being with public health officials. The veterinarians surveyed cited a need for greater interaction with physicians regarding zoonotic disease and parasitism. Veterinarians do not frequently interface with human health care professionals. Those with whom practitioners have the greatest interaction are pharmacists and representatives of drug companies. Greater interaction may benefit management of zoonotic disease and parasitism.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".