Exploring Pain Acceptance and Health Literacy as Predictors of Pain Intensity: A Cross-Sectional Study
Bibliographic record
Abstract
Objectives Chronic pain is a pervasive condition that affects millions worldwide, with significant impacts on individuals’ quality of life. This study aimed to explore the role of pain acceptance and health literacy in predicting pain intensity among individuals with chronic pain, providing insights into non-pharmacological factors that may influence pain experiences. Methods A cross-sectional design was employed, involving 350 participants from Richmond Hill, Ontario, Canada. Pain intensity was measured using the numeric pain rating scale (NPRS), while pain acceptance and health literacy were assessed using the chronic pain acceptance questionnaire (CPAQ) and the health literacy questionnaire (HLQ), respectively. Pearson correlation and linear regression analyses were conducted to examine the relationships between the study variables. Results Pain acceptance and health literacy were both significantly negatively correlated with pain intensity (r=-0.45, P<0.01 and r=-0.38, P<0.01, respectively). In the regression model, both variables significantly predicted pain intensity, accounting for 26% of the variance (R²=0.26, adjusted R²=0.24, F=19.56, P<0.001). Pain acceptance (B=-0.15, β=-0.25, t=-5.00, P<0.001) and health literacy (B=-0.10, β=-0.20, t=-4.00, P<0.001) emerged as significant predictors. Conclusion The study findings highlight the significant roles of pain acceptance and health literacy in managing pain intensity among individuals with chronic pain. These findings suggest that interventions aimed at enhancing pain acceptance and health literacy could be beneficial in reducing pain intensity and improving the quality of life for chronic pain sufferers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".