Continuing Veterinary Medical Education Needs and Delivery Preferences of Alberta Veterinarians
Bibliographic record
Abstract
The questionnaire component of a continuing-education needs assessment of Alberta veterinarians is described. A questionnaire about work characteristics, topic priorities, and program delivery preferences was mailed to all licensed Alberta veterinarians, 54% of whom responded. Topic-priority data were factor analyzed, and differences in priorities were examined using multivariate analysis of variance (MANOVA). Seven learning areas emerged: veterinary medicine, professionalism and practice management, animal welfare, livestock practice and epidemiology, prophylaxis and therapeutics, specialty diagnostics, and theriogenology. Associations between delivery preferences and work characteristics were sought using MANOVA and contingency tables analysis. Significant differences were identified in learning-area priorities and preferred program types and times when respondents were grouped by species focus, and there were significant associations between respondents' location and their preferred learning locations. There were no significant associations between location or practice size and preferred program type. Group differences support the need for events tailored by species focus. Delivery preferences suggest that workload and distance are barriers to attendance, although face-to-face events were universally preferred.
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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".