Chronic back pain and major depression in the general Canadian population
Bibliographic record
Abstract
Chronic pain and depression are two of the most common health problems that health professionals encounter, yet only a handful of epidemiological studies have investigated the relationship between these conditions in the general population. In the present study we examined the prevalence and correlates of major depression in persons with chronic back pain using data from the first cycle of Canadian Community Health Survey in a sample of 118,533 household residents. The prevalence of chronic back pain was estimated at 9% of persons 12 years and older. Rates of major depression, determined by the short-form of the Composite International Diagnostic Interview, were estimated at 5.9% for pain-free individuals and 19.8% for persons with chronic back pain. The rate of major depression increased in a linear fashion with greater pain severity. In logistic regression models, back pain emerged as the strongest predictor of major depression after adjusting for possible confounding factors such as demographics and medical co-morbidity. The combination of chronic back pain and major depression was associated with greater disability than either condition alone, although pain severity was found to be the strongest overall predictor of disability.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".