Health literacy, pain intensity and pain perception in patients with chronic pain
Bibliographic record
Abstract
BACKGROUND: Chronic pain poses a large burden for the healthcare system and the individuals concerned. The impact of health literacy (HL) on health status and health outcomes is receiving more and more attention. The aim of this study was to evaluate the association of HL with chronic pain intensity and pain perception. METHODS: A total of 121 outpatients suffering from chronic pain (pain duration >3 months) were evaluated. The HL was measured using the health literacy screening questions. Pain intensity was measured with a Visual Analogue Scale (VAS) and pain perception with the short-form McGill Pain Questionnaire (SF-MPQ). RESULTS: Individuals with low HL had significantly higher VAS values (Pearson correlation coefficient= -0.270, p = 0.003). Stepwise regression analysis showed that HL has a significant association with pain intensity (odds ratio [OR] = 2.31; 95% confidence interval [CI] 1.11-4.83), even after controlling for age and sex (OR = 2.27; 95% CI 1.07-4.82), but no longer after controlling for education (OR = 2.10; 95% CI 0.95-4.64). CONCLUSION: Individuals with a higher HL showed less pain intensity, which seems to be caused by a better pain management; therefore, supporting the development of HL in patients with chronic pain could be seen as an important objective of integrated care.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".