Bibliographic record
Abstract
OBJECTIVES: This study describes the prevalence of chronic pain among seniors living in private households and in long-term health care institutions. Associations between an increase in chronic pain and unhappiness and negative self-perceived health are examined. DATA SOURCES: Data are from the Health Institutions and Household components of Statistics Canada's 1994/1995 through 2002/2003 National Population Health Survey (NPHS) and 2005 Canadian Community Health Survey (CCHS). ANALYTICAL TECHNIQUES: Prevalence rates of chronic pain were estimated using cross-sectional data from the 1996/1997 NPHS and the 2005 CCHS. Multiple logistic regression was used to model an increase in chronic pain in relation to quality of life outcomes, controlling for chronic conditions, medication use, age, sex, proxy response, and socioeconomic status. MAIN RESULTS: Thirty-eight percent of institutionalized seniors experienced pain on a regular basis, compared with 27% of seniors living in households. In both populations, rates were higher for women than men. An increase in pain over a two-year period was associated with higher odds of being unhappy or having negative self-perceived health at the end of the period. CONCLUSIONS: Chronic pain is a major health issue for seniors, particularly those in health care institutions. The reduction of pain symptoms, independent of the presence of chronic conditions, would have a positive impact on the well-being of seniors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".