Comparison of the Perceived Quality of Life between Medical and Veterinary Students in Tehran
Bibliographic record
Abstract
Medical and veterinary professional programs are demanding and may have an impact on a student's quality of life (QOL). The aim of this study was to compare the perceived QOL of these two groups. In this study, we used the SF-36 questionnaire in which higher scores mean a better perceived QOL. Only the students in the internship phase of their program were selected so that we could compare the two groups in a similar way. In total, 308 valid questionnaires were gathered. Apart from age and body mass index (BMI), the two groups were demographically similar. The scores of five domains (physical activity limitation due to health problems, usual role limitation due to emotional problems, vitality, general mental health, and general health perception) and also the total score were statistically higher in medical students. Only the score of one domain (social activity limitation due to physical or emotional problems) was statistically higher in veterinary students. BMI, physical activity limitation due to health problems, and vitality lost their significance after binomial logistic regression. We found that, in general, veterinary students have lower scores for the perceived QOL with social function being the only exception. It can be assumed that in medical students, interaction with human patients may have a negative impact in the score of this domain. Even though medical students have shown lower perceived QOL than the general population in previous studies, veterinary students appear to have slightly lower perceived QOL than medical students.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".