Prevalence and Correlates of Likely Major Depressive Disorder among Medical Students in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Medical students are exposed to multiple factors during their academic and clinical studies that contribute to depression. AIMS: This study aims to examine the prevalence and correlates of likely major depressive disorder (MDD) among medical students. METHODS: This study utilized a descriptive cross-sectional design. Data were collected through a self-administered online survey, which included questions on sociodemographic characteristics and likely MDD using the PHQ-9. Data were analyzed using a descriptive, Chi-square test and logistic regression model. RESULTS: There were 246 medical students who participated in the survey. The majority were females, 155 (65.1%); Caucasian, 158 (66.4%); and in a relationship, 168 (70.5%). The prevalence of likely MDD was 29.1%. Respondents who did not feel supported and respondents who were neutral about their social support, friends, and family, were 11 and 4 times more likely to experience MDD than those who felt well supported (OR = 11.14; 95% CI: 1.14-108.80) and (OR = 4.65; 95% CI: 1.10-19.56), respectively. CONCLUSIONS: This study suggests a high prevalence of likely MDD among medical students who do not feel they have sufficient social support from friends and family. Social adjustments, including talking to friends and family and participating in leisure activities, could reduce the level of depression among 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".