Work disability benefits due to musculoskeletal disorders among Brazilian private sector workers
Bibliographic record
Abstract
OBJECTIVE: To evaluate the prevalence and characteristics of disability benefits due to musculoskeletal disorders (MSD) granted to Brazilian private sector workers. METHODS: This was a population-based epidemiological study of MSD-related benefits among registered private sector workers (n=32 959 329). The prevalence (benefits/10 000 workers/year) of work disability benefits was calculated by gender, age, state, Human Development Index (HDI), economic activity, MSD type and work-relatedness. RESULTS: The prevalence of MSD-related benefits in Brazil among registered private sector workers in 2008 was 93.6/10 000 workers. The prevalence increased with age, and was higher for women (112.2) than for men (88.1), although the former had shorter benefit duration. The gender-adjusted prevalence by state varied from 16.6 to 90.3 for non-work-related, and from 7.8 to 59.6 for work-related benefits. The Brazilian states with a high-very high HDI had the highest prevalence. The top four most common types of MSD-related benefits were due to back pain, intervertebral disc disorders, sinovitis/tenosynovitis and shoulder disorders. CONCLUSION: MSD is a frequent cause of work disability in Brazil. There were differences in prevalence among economic activities and between states grouped by HDI. This study demonstrates that further evaluation of the contributing factors associated with MSD-related disability benefits is required. Factors that should be considered include production processes, political organisation, socioeconomic and educational characteristics, the compensation and recording systems, and employee-employer power relationships. These factors may play an important role in the prevalence of MSD-related disability benefits, especially in countries with large socioeconomic iniquities such as Brazil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".