Adverse Childhood Experiences among Veterinary Medical Students: A Multi-Site Study
Bibliographic record
Abstract
This research explores Adverse Childhood Experiences (ACEs) among veterinary medical students across six academic institutions of veterinary medicine, and their relationship with depression, stress, and desire to become a veterinarian. Between April 1, 2016, and May 23, 2016, 1,118 veterinary medical students in all 4 years of the curriculum (39% response rate) completed an anonymous web-based questionnaire about ACEs, depression using the Center for Epidemiological Studies Depression scale (CESD), stress using the Perceived Stress Scale (PSS), and the age at which they wanted to become a veterinarian. Sixty-one percent (677) of respondents reported having at least one ACE. The most prevalent ACE reported was living with a household member with a mental illness (31%). Students who had experienced four or more ACEs had an approximately threefold increase in signs of clinical depression and higher than average stress when compared to students who had experienced no ACEs. The number of ACEs showed an overall graded relationship to signs of clinical depression and higher than average stress. There was no statistically significant relationship between age at which a student wanted to become a veterinarian and exposure to ACEs. Veterinary students report being exposed to ACEs before age 18 at a rate similar to that of other population-based studies. These findings do not suggest that veterinary students enter the veterinary medical education system more at risk for poor mental health due to ACEs than the general population.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".