Exploring Personality as a Predictor of Academic Factors and Mental Health Among Veterinary Students at the Ontario Veterinary College
Bibliographic record
Abstract
Veterinary students and veterinary professionals are at high risk for mental health issues such as depression. This thesis explores associations of personality with mental health outcomes in veterinary students for the first time. Myers-Briggs Type Indicator (MBTI) personality data were collected for eleven graduating classes of veterinary students at the Ontario Veterinary College (OVC), and mental health data were collected for one. OVC veterinary students were diverse in their MBTI distribution. Significant associations were identified between the MBTI and gender, career interests, and academic performance. Associations between the MBTI and perceived learning climate and stressors were explored. Students of the Introversion and Intuition preferences had greater odds of scoring above the research threshold for depression and scored lower on psychological capital than their opposite preferences. These associations warrant further investigation to understand the impact that personality and mental health may have on veterinary students and their academic experiences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".