Chronic back problems and labor force participation in a national population survey: impact of comorbid arthritis
Bibliographic record
Abstract
BACKGROUND: Back problems and arthritis are common chronic conditions, while having back problems is a frequent reason for lost work time. The objective of this study was to investigate employment status amongst individuals who report having both back problems and arthritis, compared to having either condition alone. METHODS: We analyzed data from the 2007/2008 Canadian Community Health Survey (ages 25-64, n = 79,719). Respondents who reported neither having worked in the past 12 months nor the past week were coded as not currently employed. Those reported being permanently unable to work were considered to be out of the labor force. Log-Poisson regressions, adjusting for socio-demographic and lifestyle factors, were used to estimate risks for being not currently employed or being out of the labor force for 5 mutually exclusive groups of chronic conditions: arthritis and back problems, back problems, arthritis, any other chronic conditions, and no chronic conditions. RESULTS: 12.7% of respondents reported being not currently employed and 2.9% being out of the labor force. 5.8% of respondents reported both arthritis and back problems, while 16.1% reported back problems and 7.3% arthritis. The back problems and arthritis group had the highest risk of not being currently employed. The risk was higher for men (PR = 1.90; 95% CI = 1.58, 2.29) than for women (PR = 1.31; 95% CI = 1.18, 1.46). Risks of being permanently unable to work were also the greatest for those with comorbid back problems and arthritis. CONCLUSIONS: There is a need for a reappraisal of back problems as a cause of work disability to account for the possibility of co-occurring arthritis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".