Assessment of Depression and Health-Related Quality of Life in Veterinary Medical Students: Use of the 2-Item Primary Care Evaluation of Mental Disorders Questionnaire (PRIME-MD PHQ) and the 8-Item Short Form-8 Survey (SF-8)
Bibliographic record
Abstract
Depression and health-related quality of life (HRQOL) are major concerns affecting veterinary students' well-being. Shorter versions of instruments to assess depression and HRQOL are timesaving and preferable. To the authors' knowledge there are no studies available that assess HRQOL in veterinary students. The objectives of this study were to screen veterinary students for depression during two semesters using a 2-item Primary Care Evaluation of Mental Disorders Procedure Health Questionnaire (PRIME-MD PHQ), and to assess HRQOL over two semesters using the Optum Short Form-8 (SF-8) Health Survey. A cohort of 273 students from two classes were invited to complete the PRIME-MD PHQ and the SF-8 survey during the fall semester of their first year, and again in the spring semester of the second year. Descriptive statistics, factor analysis, multiple regression, and logistic regression were used to perform data analysis. The proportion of students with symptoms of depression was high, ranging from 37.4% to 56.8% between the two classes. The SF-8 survey indicated a mental component summary (MCS) score of <50, indicating poor mental health for both classes, whereas the physical component summary (PCS) was >50, suggesting good physical health. Female students (p =.043) had low MCS scores compared to males. Students from both classes had lower MCS scores in spring compared to fall (p =.019). The PRIME-MD PHQ and the SF-8 were acceptable instruments for assessing depression and HRQOL in veterinary students, respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".