Mental health in medical, dental and pharmacy students: A cross-sectional study
Bibliographic record
Abstract
Background: The mental health of health students is considered a public health issue which increased dramatically with the COVID 19's pandemic. Few studies have assessed the prevalence of depression in medical, pharmacy, and dental students. Our goal was to assess mental health in health students from the same university and identify the associated factors. Methods: An online survey was sent to the health students of the University of Paris in 3 specialties (medicine, pharmacy, and dentistry). We used the Hospitalization Anxiety and Depression scale, the Composite International Diagnostic Interview-Short Form and the Maslach Burnout Inventory (with 2 versions: the Human Services Survey for clinical students and residents and the Student survey for the others). The presence of suicidal ideation, humiliation, sexual harassment, and sexual aggression over twelve-months was also measured. We performed multivariable logistic regression analyses to identify the associated factors of Major Depressive Episodes (MDE). Findings: 1925 students answered the survey. The overall prevalence of 7-day anxiety and depressive symptoms, MDE, suicidal ideation, humiliation, sexual harassment, and sexual aggression were 55%, 23%, 26%, 19%, 19%, 22%, and 5.5%, respectively. Burnout was present in 42% of nonclinical students and 65% of clinical students and residents. Multivariable logistic regression identified several associated factors of MDE: moderate (OR = 1.49,CI95[1.17-1.90]) or major (OR = 2.32,CI95[1.68-3.20]) subjective financial difficulties, humiliation (OR = 1.71,CI95[1.28-2.28]), sexual abuse (OR = 1.65,CI95[1.04-2.60]), and sexual harassment (OR = 1.60,CI95[1.19-2.16]). Interpretation: This is one of the largest studies comparing dental, pharmacy and medical students from the same university. We found elevated prevalences of psychiatric symptoms with variation depending on specialty.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".