Prevalence/Incidence of Low Back Pain and Associated Risk Factors Among Nursing and Medical Students: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: To summarize evidence regarding the prevalence and incidence of low back pain and associated risk factors in nursing and medical students. TYPE: Systematic review and meta-analysis. LITERATURE SURVEY: The protocol was registered with PROSPERO (CRD42015029729). Its reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Seven databases were searched until August 2020 to identify relevant studies. METHODOLOGY: Two independent reviewers screened, extracted, and evaluated the risk of bias of the selected studies. Meta-analyses were used to estimate 12-month prevalence/incidence rates of low back pain and associated risk factors in these students. Levels of evidence for risk factors were determined by the updated Guidelines for Systematic Reviews in the Cochrane Collaboration Back Review Group. SYNTHESIS: Sixteen studies involving 7072 students were included. The pooled 12-month prevalence rates of low back pain for nursing and medical students were 44% (95% confidence interval [95% CI]: 27%-61%) and 53% (95% CI: 44%-62%), respectively. The 12-month incidence of low back pain in nursing students ranged from 29% to 67%. No incidence rate was reported in medical students. Strong/moderate-quality evidence supported that final year of study (pooled odds ratio [OR] from five studies, 1.96, 95% CI: 1.13-3.40), anxiety (OR ranging from 3.12 to 4.61), or high mental pressure or psychological distress (OR ranging from 1.37 to 4.52) was associated with a higher 12-month low back pain prevalence in both student groups. Moderate-quality evidence suggested that prior history of low back pain (pooled OR from two studies: 3.46, 95% CI: 1.88-6.36) was associated with a higher 12-month low back pain incidence in nursing students. Similarly, moderate-quality evidence suggested that female medical students (pooled OR from two studies: 1.77, 95% CI: 1.09-2.86) demonstrated a higher 12-month low back pain prevalence than male counterparts. CONCLUSIONS: Although it is impossible to alter nonmodifiable risk factors for low back pain, universities may develop and implement proper strategies to mitigate modifiable risk factors in these students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.048 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".