Low Back Pain Among Medical Students: A Meta-Analysis of a Growing Global Public Health Concern
Bibliographic record
Abstract
Abstract Background Low back pain (LBP) is a significant public health issue. Recent literature highlights the substantial burden of low back pain among medical students due to academic demands; however, a comprehensive global perspective on this issue remains lacking. This meta-analysis aims to assess the global prevalence of LBP among medical students and examine the associations. Methods A systematic review and meta-analysis were conducted, with searches in five major databases up until February 2025. Studies assessing LBP prevalence using the modified Nordic questionnaire were included. The AXIS tool was used for quality assessment. Random-effects meta-analysis was performed to calculate pooled estimates of LBP prevalence and odds ratios, with subgroup analyses by training stage, gender, and BMI. Results Thirty-two studies representing 15,819 medical students, were included which demonstrated a high-quality reporting. Meta-analyses showed significant pooled 12-month (-0.401, 95% CI[-0.402 - -0.401], p < 0.001) and 7-day (-1.339, 95% CI[-1.339 - -1.338], p < 0.001) LBP prevalence, with low heterogeneity (I² = 0%, p = 1). Clinical students had higher odds of LBP compared to preclinical students (OR = 0.924, 95% CI[0.784-1.064], p < 0.001). Female students had significantly higher odds than males (OR = 0.719, 95% CI[0.494-0.944], p < 0.001). Overweight students had elevated odds of LBP (OR = 0.893, 95% CI[-0.379-1.408], p = 0.007), while normal BMI, underweight and obese showed no significant association. Discussion LBP is a global public health issue among medical students, with clinical-stage students, females, and those with abnormal BMI (obese, underweight) being at higher risk. These findings underscore the need for targeted interventions, including gender-sensitive strategies to address LBP and reduce its impact on future healthcare professionals. Key messages • This meta-analysis of 15,819 medical students revealed female gender, clinical stage of study and being overweight as significantly increasing the odds of back pain worldwide. • Low back pain among medical students is a global public health concern necessitating targeted interventions are needed to reduce the burden of this occupational hazard.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.049 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.004 | 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 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".