Arthritis and employment research: where are we? Where do we need to go?
Bibliographic record
Abstract
Studies of work disability among individuals with arthritis reveal that loss of employment is a common, important, and costly problem. Arthritis and musculoskeletal conditions are the leading cause of longterm work disability in Canada and the US, with an estimated yearly cost of 13.7 billion dollars in Canada. In rheumatoid arthritis, reported rates of work disability are remarkably high, ranging from 32% to 50% 10 years after RA onset, and increasing to 50% to 90% after 30 years. Studies have shown that work disability starts early in the course of RA, emphasizing the need for early intervention. To date, research in the area of arthritis and employment has mostly focused on measuring the extent of the problem and on identifying predictors of work loss. Despite the importance of the problem, there has been little intervention research assessing the effectiveness of medical treatment and few interventions specifically aimed at employment, reducing work loss, or improving ability to work. Research needed includes evaluating the effect of current therapies on employment outcomes, and studying interventions specifically aimed at employment, as well as addressing methodological issues in employment research.
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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 | 0.089 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".