Prevalence and Determinants of Pain and Pain‐Related Disability in Urban and Rural Settings in Southeastern Ontario
Bibliographic record
Abstract
BACKGROUND: Canadian chronic pain prevalence estimates range from 11% to 66%, are affected by sampling and measurement bias, and largely represent urban settings. OBJECTIVES: To estimate chronic pain prevalence and factors associated with pain in southeastern Ontario, a region with a larger rural than urban residence. METHODS: A systematic sampling with a random start was used to contact households. A telephone-administered questionnaire using the Graded Chronic Pain Scale, with questions on health care and medication use, health status, depression and demographics, was administered to consenting adults (18 to 94 years of age; mean age 50.2+/-16.6 years). RESULTS: The response rate was 49% (1067 of 2167), with 76% reporting some pain over the past six months. Low pain intensity with low pain interference prevalence was 34% (grade I), high pain intensity with low pain interference was 26% (grade II), and high pain intensity with high pain interference was 17% (grades III and IV). Of those reporting pain, 49% reported chronic pain (ie, pain for a minimum of 90 days over the past six months) representing 37% of the sample. Being female, unmarried, lower income, poorer self-reported health status and rural residence were associated with increasing pain. Once depression was considered in this pain analysis, residence was no longer significant. Lower rates of health care utilization were reported by rural residents. In those reporting the highest pain grades, poor health, greater medication and health care use, depression and more pain sites were associated with higher odds for pain-related disability. CONCLUSION: There is an elevated prevalence of pain in this almost equally split rural/urban region. Further examination of health care utilization and depression is suggested in chronic pain prevalence research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".