Factors Associated with Mental Distress Among Medical Students of Universitas Pembangunan Nasional Veteran Jakarta
Bibliographic record
Abstract
Background: Mental distress refers to common mental disorders, such as depression, anxiety, and somatic symptoms, and is considered public health problem. It is reported that university students tend to have higher levels of mental distress compared to the general population, especially in medical students.Objective: The purpose of this study is to determine the risk factors affecting mental distress among medical students.Methods: A cross-sectional study using a survey was conducted among preclinical medical students at UPN Veteran Jakarta in June 2020. This study used proportional stratified sampling to complete questionnaires including demographic characteristics, adverse childhood experiences (ACE), family APGAR, and self-reporting questionnaire (SRQ-20). Data were analyzed by using logistic regression.Results: Among 138 participants, 36.2% had at least one of ACE, 55.1% of students came from a family with dysfunction, and 36.2% experienced mental distress. In multivariate analyses, some essential factors associated with mental distress are gender (OR=12.059, 95% CI: 2.311,62.916), adverse childhood experiences (OR=3.080, 95% CI: 1.903,4.983), family function (OR=2.733, 95% CI:1.097,6.809), and family structure (OR=0.290, 95% CI: 0.085,0.984).Conclusion: Students who are female, have history of adverse childhood experience, come from family with dysfunction, or non- nuclear family structure are more likely to be screened positive for mental distress. This study recommends an urgency of counselling service availability for medical students and community awareness to build a healthy family environment.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".