Prevalence of musculoskeletal disorders and associated risk factors in Canadian university students
Bibliographic record
Abstract
Abstract Background: Musculoskeletal disorders (MSKDs) present a significant burden to health care systems worldwide. Evidence suggests that university students may have unique risk factors for developing MSKDs; however, research on the corresponding prevalence and risk factors of MSKDs in Canadian students is limited. Methods: Using a multi-year cross-sectional survey, we aimed to understand the prevalence and risk factors of MSKDs in students at McMaster University. A survey on the prevalence of MSKD as well as potential risk factors was conducted online in the years 2018-2022. Our outcomes were the prevalence of MSKDs over the last 7 days and the last 12 months, as well as presence of lower body, upper body, and spine MSKDs. We investigated risk factors using negative binomial regression analysis, including a sex-stratified analysis. Results: There were a total of 289 respondents in 2018 with a decrease in the number of participants in the subsequent survey years (n2019 = 173, n2020 = 131, n2021 = 76). Participants reported a median of 2-3 pain sites in the last year and 1-2 pain sites in the last week in all four years. The most prevalent sources of self-reported pain were the lower back and neck. Depending on the year and outcome studied, 59-67% of participants reported neck/lower back pain in the last year, and 43-49% of respondents reported it in the last week. Although risk factors were different depending on the year and the sex, overall, poorer mental health, being in health care studies, regular sports participation (males only), older age, and less hours of sleep were significantly associated with higher prevalence of MSKDs. Conclusions: This identified that MSKDs is a prevalent source of pain in university students. While some risk factors, such as mental health, are known to play a role in developing MSKDs, sports activity and academic pressure are risk factors that are unique to students. Our study also suggests that there may also be differences in risk factors between sexes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".