An Examination of Myers-Briggs Type Indicator Personality, Gender, and Career Interests of Ontario Veterinary College Students
Bibliographic record
Abstract
= 1,249), and to evaluate its associations with gender and career interests. This was achieved by collecting pre-matriculation data from 11 graduating classes. Overall, OVC veterinary students were diverse in their MBTI types and preferences, as well as career interests. Extraversion, Sensing, Thinking, and Judging were the most prevalent preferences. Female veterinary students were 2.96 (95% CI = 2.11-4.17) times more likely to demonstrate the Feeling preference and 1.89 (95% CI = 1.41-2.56) more likely to prefer Judging, compared to male students (who were more likely to prefer the Thinking and Perceiving preferences, respectively). At entry to the veterinary program, students who preferred Intuition (vs. Sensing) were 2.08 (95% CI = 1.33-3.33) times more likely to be interested in a veterinary career other than practice, and 1.92 (95% CI = 1.43-2.56) times more likely to be undecided about their future veterinary career path. Both at entry to the program and in their final-year stream choice, students of the Thinking preference were more likely to select equine or food animal, rather than small animal practice, compared to students of the Feeling preference. There were additional significant associations regarding MBTI preferences and career interests. This study highlights the diversity of veterinary students, and provides an opportunity for educators to potentially expand their teaching methods and career guidance resources to better reach students of all MBTI preferences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".