Psychosocial risk factors for low back pain in US workers: Data from the 2002–2018 quality of work life survey
Bibliographic record
Abstract
BACKGROUND: Examining workplace psychosocial risk factors for back pain becomes increasingly important because of the changing nature of work and rising healthcare costs. Some psychosocial risk factors for back pain, such as work and family imbalance, exposure to a hostile work environment, and job insecurity, are understudied for the working population in the United States. METHODS: Data used in this study came from the Quality of Work Life Survey (QWL), a supplementary module of the General Social Survey conducted in the United States. Data from the 2002, 2006, 2010, 2014, and 2018 QWL surveys were used in these analyses, giving a total sample size of 6661. Five domains of workplace psychosocial risk factors for back pain were examined, including job strain, low social support, work-family imbalance, exposure to a hostile work environment (harassment and discrimination), and job insecurity. The adjusted odds ratio (aOR) of each psychosocial risk factor for back pain with 95% confidence intervals (CI) was estimated using a multivariable logistic regression model after controlling for job physical factors, occupation, and demographic and socioeconomic characteristics. RESULTS: Significant associations were found between back pain and several psychosocial factors including job strain (aOR 1.19; CI 1.00,1.41), work-family imbalance (aOR,1.42; CI 1.22,1.64), harassment (aOR 1.40; CI 1.15,1.71), and discrimination (aOR 1.20 CI 1.00,1.44). CONCLUSION: This study contributes to the understanding of the relationship between a variety of workplace psychosocial factors and back pain. Our findings suggest directions in future longitudinal research to examine emerging workplace psychosocial factors for back pain.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".