Can Cross Country Differences in Return-to-Work After Chronic Occupational Back Pain be Explained? An Exploratory Analysis on Disability Policies in a Six Country Cohort Study
Bibliographic record
Abstract
INTRODUCTION: There are substantial differences in the number of disability benefits for occupational low back pain (LBP) among countries. There are also large cross country differences in disability policies. According to the Organization for Economic Cooperation and Development (OECD) there are two principal policy approaches: countries which have an emphasis on a compensation policy approach or countries with an emphasis on an reintegration policy approach. The International Social Security Association initiated this study to explain differences in return-to-work (RTW) among claimants with long term sick leave due to LBP between countries with a special focus on the effect of different disability policies. METHODS: A multinational cohort of 2,825 compensation claimants off work for 3-4 months due to LBP was recruited in Denmark, Germany, Israel, the Netherlands, Sweden, and the United States. Relevant predictors and interventions were measured at 3 months, one and 2 years after the start of sick leave. The main outcome measure was duration until sustainable RTW (i.e. working after 2 years). Multivariate analyses were conducted to explain differences in sustainable RTW between countries and to explore the effect of different disability policies. RESULTS: Medical and work interventions varied considerably between countries. Sustainable RTW ranged from 22% in the German cohort up to 62% in the Dutch cohort after 2 years of follow-up. Work interventions and job characteristics contributed most to these differences. Patient health, medical interventions and patient characteristics were less important. In addition, cross-country differences in eligibility criteria for entitlement to long-term and/or partial disability benefits contributed to the observed differences in sustainable RTW rates: less strict criteria are more effective. The model including various compensation policy variables explained 48% of the variance. CONCLUSIONS: Large cross-country differences in sustainable RTW after chronic LBP are mainly explained by cross-country differences in applied work interventions. Differences in eligibility criteria for long term disability benefits contributed also to the differences in RTW. This study supports OECD policy recommendations: Individual packages of work interventions and flexible (partial) disability benefits adapted to the individual needs and capacities are important for preventing work disability due to LBP.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".