The Impact of Sampling and Measurement on the Prevalence of Self‐Reported Pain in Canada
Bibliographic record
Abstract
BACKGROUND: Pain is an important public health problem in Canada. International estimates of general population pain prevalence range from 2% to 46%. OBJECTIVES: The purpose of this paper is to critically examine the potentially misleading use of overall prevalence estimates in the pain literature and to use two Canadian population-based surveys to assess the impact of sampling and measurement on prevalence. METHODS: Two of the secondary data sets used were the 1996/97 National Population and Health Survey (NPHS) and the Canadian Multicentre Osteoporosis Study (CaMos). This paper is based on the assessment of chronic pain in the NPHS, and the assessment of short term pain using the Medical Outcomes Trust's 36-item health survey and the Health Utilities Index, both collected by CaMos. Data are presented as frequencies and percentages overall and stratified by age and sex. CaMos prevalence estimates were age- and sex-standardized to the NPHS population. RESULTS: The overall prevalence of pain was 39% for one-week pain, 66% for four-week pain and 15% for chronic pain. Women were more likely to report pain than men and the prevalence of pain increased with age. CONCLUSIONS: This study yields useful information about the self-reported responses to a variety of questions assessing pain in the general population. Responses to the different questions likely represent different categories of pain, such as short term versus chronic pain, which in turn may have different epidemiological risk factors and profiles. Longitudinal studies of the epidemiology, predictors and natural history of chronic pain are urgently needed in the 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 | 0.050 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".