Factors Associated with Chronic Noncancer Pain in the Canadian Population
Bibliographic record
Abstract
Chronic noncancer pain (CNCP) is a prevalent health problem with pervasive negative effects on the individual's quality of life. Previous epidemiological studies of CNCP have suggested a number of individual biological, psychological and societal correlates of CNCP, but it has rarely been possible to simultaneously compare the relative strengths of many such correlates in a Canadian population sample. With data provided by the 1996/1997 Canadian National Population Health Survey, ordinal logistic regression was used to examine the extent to which a number of population variables are associated with CNCP in a large (n=69,365) dataset. The analysis revealed cross-sectional correlations of varying strengths between CNCP and 27 factors. Increasing age, low income, low educational achievement, daily cigarette smoking, physical inactivity and abstention from alcohol were among the factors found to increase CNCP risk. The considerable impact of distress and depression on CNCP are also highlighted. A number of comorbid medical illnesses increased CNCP risk, including some (such as chronic obstructive pulmonary disease, epilepsy and thyroid disease) that have not hitherto been associated with pain. White race and the affirmation of an important role for spirituality or faith reduced CNCP risk. In contrast to some previous studies, female sex did not emerge as an independent CNCP risk. The present exploratory analysis describes associations between CNCP and a number of characteristics from several domains, thus suggesting many areas for further 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".