How Does Emotional Intelligence Fit into the Paradigm of Veterinary Medical Education?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it