Risk Factors Associated With Back Pain: A Cross-Sectional Study of 963 College Students
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate standard measures of health behavior for association with back pain among college students using data from the standardized National College Health Assessment survey. This investigation evaluated potential risk factors among a population of students at a Colorado university. METHODS: This cross-sectional study included 963 survey results that were assessed using backward selection logistic regression techniques to evaluate the associations between common college-life health behaviors and back pain occurrence within the past school year. RESULTS: Thirty-eight percent of college students surveyed reported having back pain within the past school year. Investigators found that univariate associations included multiple domains, but only psychosocial factors remained statistically significant in a final regression model and were associated with back pain. Feeling chronically fatigued (odds ratio, 3.89; 95% confidence interval, 1.09-13.86) and being in an emotionally abusive relationship (odds ratio, 2.78; 95% confidence interval, 1.69-4.57) were the factors most strongly associated with back pain in the final model. CONCLUSIONS: Psychosocial factors were identified to be associated with back pain. The prevalence of back pain among this younger population is of significant concern and warrants further investigation to identify contributing factors that may help in the development of interventions to reduce the epidemic of back pain within college students and lessen the burden upon college health providers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".