The prevalence, risk factors, and psychosocial impacts of acne vulgaris in medical students: a literature review
Bibliographic record
Abstract
Affecting approximately 9.4% of the population worldwide, acne vulgaris is a common chronic inflammatory skin disease. Medical students are exposed to higher levels of stress and have a higher prevalence of acne. However, the risk factors and the impact of acne on medical students' mental health remains poorly understood. The aim of this literature review is to: (i) summarize the prevalence and risk factors of acne vulgaris in medical students and (ii) highlight the impact of psychological consequences of acne in medical students. A literature search was conducted using MEDLINE and EMBASE in OVID, using variations in the following search terms: acne vulgaris, medical students, self-esteem, psychology, psychiatry, suicide, suicidal thoughts, self-harm, positive and negative effects, psychological well-being, anxiety, and depression. Studies that stated the prevalence or risk factors of acne vulgaris and/or examined the association between psychosocial effects and acne vulgaris in medical students were included. Eleven cross-sectional studies were included. The prevalence of acne vulgaris in medical students ranged from 34.38% to 97.9% across nine studies. Review of these articles revealed that acne prevalence is associated with stress, gender differences, and lifestyle factors in medical students. Acne had many negative psychological and social impacts on medical students including negative self-image, lower confidence, embarrassment, depression, anxiety, social withdrawal, and impaired social behaviors. Further research on the intersection between acne vulgaris and the mental health of medical students is needed.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".