New Directions for Veterinary Technology
Bibliographic record
Abstract
Veterinary technology has generally established itself well in companion-animal and mixed-animal veterinary medical practice, but the career's growth trajectory is uncertain. Michigan State University (MSU) convened a national conference, "Creating the Future of Veterinary Technology-A National Dialogue," in November 2011 to explore ways to elevate the veterinary technician/technologist's role in the veterinary medical profession and to identify new directions in which the career could expand. Veterinary technicians/technologists might advance their place in private practice by not only improving their clinical skills, but by also focusing on areas such as practice management, leadership training, business training, conflict resolution, information technology, and marketing/communications. Some new employment settings for veterinary technicians/technologists include more participation within laboratory animal medicine and research, the rural farm industry, regulatory medicine, and shelter medicine. Achieving these ends would call for new training options beyond the current 2-year and 4-year degree programs. Participants suggested specialty training programs, hybrid programs of various types, online programs, veterinary technician residency programs of 12-18 months, and more integration of veterinary technician/technology students and veterinary medicine students at colleges of veterinary medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".