Prevalence of musculoskeletal complaints in female university students participating in sports activities at the University of Lagos
Bibliographic record
Abstract
Objectives: Musculoskeletal complaints (MSCs) are among the most reported health issues globally and a leading cause of disability in younger populations, including university students. This study investigated the prevalence of MSCs among female students at the University of Lagos who engage in sporting activities. Methodology: A cross-sectional analytical survey was conducted among 363 female university students recruited across various departments. Data collection involved the use of Nordic Musculoskeletal Questionnaire to assess MSCs prevalence. The self-administered questionnaire captured reports of musculoskeletal pain over the past 12-months. Descriptive statistics, including mean, frequency, percentages, and tables, were used to summarize demographic variables, sport-related characteristics and medical consultations, while chi-square tests examined associations between sports participation and MSCs occurrence at an alpha level of 5%. Results: Participants had a mean age of 20.30 years. The lower limb was most affected, with 203 (56%) reporting hip/thigh pain, followed by the shoulder 182(50.2%), neck 176(48.5%), lower back 167(46.1%), ankle/foot 160(44.1%), and knee 148(40.8%). Among those affected, 35.2% reported activity limitations due to hip or thigh pain. Additionally, a significant association was found between sport duration and 12-month prevalence of musculoskeletal complaints in the shoulder (p = 0.008) and lower back (p = 0.036). Conclusion: MSCs were highly prevalent among female university students engaged in sports, with the lower limbs being the most affected. Awareness and preventive strategies are essential to reduce the risk and long-term impact of MSCs in this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".