Mental health in medical, dental and pharmacist students: a cross-sectional study
Bibliographic record
Abstract
Introduction Health student’s mental health is considered a public health issue that dramatically increased with COVID 19’s pandemic. However, very few studies assessed the prevalence of mental health in medical, pharmacist, and dental students. Objectives Our goal was to assess mental health in health students from the same university one year after pandemic’s beginning and look at for associated factors. Methods An online survey was realized in Paris university which has the 3 specialties (medicine, pharmacy, and dental). We used the Hospitalization Anxiety and Depression scale, Composite International Diagnostic Interview - Short Form questionnaire, Maslach Burnout Inventory (with 2 versions (Student survey and Human Services Survey). We also asked for 12 months of suicidal ideation, humiliation, sexual harassment, and sexual aggression. We did multivariable logistic regression analyses to identify Major Depressive Episode (MDE) associated factors. Results We included 1925 students: 95 dental, 233 pharmacists, 541 medical preclinic, 587 medical clinic and 469 residents. Overall prevalence of 7- days anxiety symptoms, 7- days depressive symptoms, 12-month MDE, 12-month suicidal ideation, humiliation, sexual harassment and sexual aggression were 55%, 23%, 26%, 19%, 19%, 22% and 6% respectively. There were significative differences between groups for anxiety and depressive symptoms and MDE (p<0.001 for all). Associated factors to MDE in multivariable logistic regression were humiliation (OR=1.71, IC95[1.28-2.28]), sexual harassment (OR=1.60, IC95[1.19-2.16]), sexual abuse (OR=1.65, IC95[1.04-2.60])) and moderate (OR=1.49, IC95[1.17-1.90]) or important (OR=2.32, IC95[1.68-3.20]) subjective financial difficulties. Conclusions Health student’s prevalence of psychiatric symptoms is significant, but it seems possible to intervene on several risk factors. Disclosure No significant relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".