The prevalence of feeding and eating disorders symptomology in medical students: an updated systematic review, meta-analysis, and meta-regression
Bibliographic record
Abstract
Medical students have a higher risk of developing psychological issues, such as feeding and eating disorders (FEDs). In the past few years, a major increase was observed in the number of studies on the topic. The goal of this review was to estimate the prevalence risk of FEDs and its associated risk factors in medical students. Nine electronic databases were used to conduct an electronic search from the inception of the databases until 15 th September 2021. The DerSimonian–Laird technique was used to pool the estimates using random-effects meta-analysis. The prevalence of FEDs risk in medical students was the major outcome of interest. Data were analyzed globally, by country, by research measure and by culture. Sex, age, and body mass index were examined as potential confounders using meta-regression analysis. A random-effects meta-analysis evaluating the prevalence of FEDs in medical students ( K = 35, N = 21,383) generated a pooled prevalence rate of 17.35% (95% CI 14.15–21.10%), heterogeneity [ Q = 1528 (34), P = 0.001], τ 2 = 0.51 (95% CI 0.36–1.05), τ = 0.71 (95% CI 0.59–1.02), I 2 = 97.8%; H = 6.70 (95% CI 6.19–7.26). Age and sex were not significant predictors. Body mass index, culture and used research tool were significant confounders. The prevalence of FEDs symptoms in medical students was estimated to be 17.35%. Future prospective studies are urgently needed to construct prevention and treatment programs to provide better outcomes for students at risk of or suffering from FEDs. Level I, systematic review and meta-analysis.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.055 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".