Bibliographic record
Abstract
OBJECTIVE: To measure the effect of arthritis and musculoskeletal conditions on working life expectancy. METHODS: Cross sectional data from the 1994 Canadian National Population Health Survey (NPHS) were used to calculate and compare the working life expectancy of individuals who reported "arthritis or rheumatism" with that of the general population. Age and sex-specific workforce participation rates were calculated for the population reporting arthritis or rheumatism as a chronic condition, excluding back pain, and for the entire population surveyed. Age and sex-specific population figures and mortality data were obtained from annual estimates produced by Statistics Canada. Working life expectancy was estimated by constructing multiple-decrement life tables for the total and for the arthritis and rheumatism populations. RESULTS: The NPHS surveyed 22,000 households, yielding a sample size of 58,439 individuals. The percentage of the population aged 15 to 65 yrs who reported having arthritis or rheumatism was 8.9%. The percentage of persons employed for each group was reduced compared to the total population, by 3 to 23%. Working life expectancy of individuals with arthritis or rheumatism was reduced by 4.19 +/- 0.02 yrs (mean +/- SE) for men and 3.12 +/- 0.01 yrs for women at age 15 (p < 0.001 for both), with a persistent reduction through all age groups. Working life expectancy of men at age 15 was 37.42 +/- 0.01 yrs for the population with arthritis or rheumatism compared to 41.62 +/- 0.01 yrs for the total population; for women it was 31.06 +/- 0.01 and 34.19 +/- 0.001 yrs for both groups, respectively. CONCLUSION: The working life expectancy of people with arthritis and musculoskeletal conditions is significantly reduced compared to the general Canadian population.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".