Pain Management Strategies and Health Care Use in Community-Dwelling Individuals Living with Chronic Pain
Bibliographic record
Abstract
OBJECTIVE: To describe factors associated with high clinic and emergency room (ER) use among individuals with chronic pain. DESIGN: This study is part of a larger cross-sectional survey on the epidemiology of chronic pain in Canada. The current analysis was guided by the Andersen-Newman Service Utilization Model. METHODS: Respondents (N = 702) were grouped into high (top 10%) and low (bottom 90%) users based on the number of visits made to clinics and ERs over the past year. The two groups were compared on predisposing (e.g., pain self-efficacy and sociodemographic characteristics), enabling (e.g., income and education), and need (e.g., pain characteristics and number of comorbidities) factors as well as personal health behaviors (e.g., use of medications). Binary logistic regression analysis was used to identify characteristics associated with high use in each setting. RESULTS: High users were defined as 30 or more clinic visits or one or more ER visits. The factors associated with high clinic use in the adjusted analysis were low pain self-efficacy (odds ratio [OR] = 2.60, 95% confidence interval [CI] = 1.50-4.51), two or more comorbidities (OR = 2.13, 95% CI = 1.23-3.69), five or more pain sites (OR = 2.30, 95% CI = 1.28-4.14), and having an "other" pain diagnosis (OR = 1.78, 95% CI = 1.01-3.20). Factors that increased ER use were low pain self-efficacy (OR = 2.01, 95% CI = 1.28-3.15) and two or more comorbidities (OR = 2.31, 95% CI = 1.48-3.59), while use of alternative pain management strategies reduced ER use (OR = 0.42, 95% CI = 0.21-0.84). CONCLUSIONS: Longitudinal studies are needed to confirm if modifiable factors such as pain self-efficacy and use of alternative therapies reduce health care use.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".