Are job strain and sleep disturbances prognostic factors for low-back pain?A cohort study of a general population of working age in Sweden
Bibliographic record
Abstract
The aim of this study was to determine whether job strain, i.e. a combination of job demands and decision latitude (job control), and sleep disturbances among persons with occasional low-back pain are prognostic factors for developing troublesome low-back pain; and to determine whether sleep disturbances modify the potential association between job strain and troublesome low-back pain. A population-based cohort from the Stockholm Public Health Cohort surveys in 2006 and 2010 (= 25,167) included individuals with occasional low-back pain at baseline 2006 (= 6,413). Through logistic regression analyses, potential prognostic effects of job strain and sleep disturbances were studied. Stratified analyses were performed to assess modification of sleep disturbances on the potential association between job strain and troublesome low-back pain. Those exposed to job strain; active job (odds ratio (OR) 1.3, 95% confidence interval (95% CI) 1.1-1.6), or high strain (OR 1.5, 95% CI 0.9-2.4) and those exposed to severe sleep disturbances (OR 3.0, 95% CI 2.3-4.0), but not those exposed to passive jobs (OR 1.1, 95% CI 0.9-1.4) had higher odds of developing troublesome low-back pain. Sleep disturbances did not modify the association between job strain and troublesome low-back pain. These findings indicate that active job, high job strain and sleep disturbances are prognostic factors for troublesome low-back pain. The odds of developing troublesome low-back pain due to job strain were not modified by sleep disturbance.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".