How Does Emotional Intelligence Fit into the Paradigm of Veterinary Medical Education?
Bibliographic record
Abstract
The term ''emotional intelligence'' (EI) has become very popular in the business world and has recently infiltrated veterinary medical education. The term purports to encompass those qualities and skills that are not measured by IQ tests but do play an important role in achieving success in life. Veterinary medical educators often incorporate these in a category called ''non-technical competencies'' (which includes, for example, communication skills) and acknowledge that veterinarians need more training in this area in order to be successful. Although EI looks promising as a means for teaching these non-technical competencies to students and practitioners, there are some challenges to its application. To begin with, there are three competing models of EI that differ in definition and measuring instruments. Although some research has suggested that high EI is associated with success in school and in business, there are no studies directly correlating high EI with greater success in the veterinary profession. Nor have any studies confirmed that increasing a student's EI will improve eventual outcomes for that student. It is important that educators approach the implementation of new techniques and concepts for teaching non-technical competencies the same way they would approach teaching a new surgical technique or drug therapy. EI is an intriguing and promising construct and deserves dedicated research to assess its relevance to veterinary medical education. There are opportunities to investigate EI using case control studies that will either confirm or discredit the benefits of incorporating EI into the veterinary curriculum. Implementing EI training without assessment risks wasting limited resources and alienating students.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".