Psychosocial Factors Influencing Quality of Life Among Medical Students
Bibliographic record
Abstract
OBJECTIVE: This study investigated the influence of psychosocial factors on medical students' quality of life (QOL). METHODS: A total of 408 medical students participated in this study. We collected data on participants' sociodemographic details, symptoms of depression and Internet addiction, self-esteem, social support, and QOL. QOL was assessed using the World Health Organization Quality of Life-Abbreviated form, which has four domains (physical health, psychological health, social relationships, and environment). A stepwise multiple linear regression model was constructed to identify factors' independent impact on QOL. RESULTS: Higher levels of depression and Internet addiction were associated with lower scores in all domains of QOL, whereas higher levels of self-esteem and social support were associated with higher scores. Being in third-year versus first-year was associated with higher scores in the physical health and environment domains. Living alone or in dormitories, low or middle socioeconomic status, and insufficient or moderate pocket money were associated with lower scores in the environment domain. Additionally, female students displayed significantly lower scores for physical health, psychological health, and environment than male students, but not for social relationships. There were significant differences in certain domains of QOL due to sociodemographic factors. CONCLUSION: This study demonstrates the psychosocial factors influencing medical students' QOL. Educational strategies focusing on strengthening self-esteem and social support as well as preventing depression and Internet addiction may contribute to improving medical students' QOL.
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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 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".