Patient enablement and health-related quality of life for patients with chronic back and knee pain: a cross-sectional study in primary care
Bibliographic record
Abstract
Background Chronic back and knee pain impairs health- related quality of life (HRQoL) and patient enablement can improve HRQoL. Aim To determine whether enablement was a moderator of the effect of chronic back and knee pain on HRQoL. Design and setting A cross-sectional study of Chinese patients with chronic back and knee problems in public primary care clinics in Hong Kong. Method Each participant completed the Chinese Patient Enablement Instrument-2 (PEI-2), the Chinese Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the Pain Rating Scale (PRS). Multivariable regression examined the effects of PRS score and PEI-2 score on WOMAC total score. A moderation regression model and simple slope analysis were used to evaluate whether the interaction between enablement (PEI-2) and pain (PRS) had a significant effect on HRQoL (WOMAC). Results Valid patient-reported outcome data from 1306 participants were analysed. PRS score was associated with WOMAC total score (β = 0.326, P <0.001), whereas PEI-2 score was associated inversely with WOMAC total score (β = −0.260, P <0.001) and PRS score. The effect of the interaction between PRS and PEI-2 (PRS × PEI-2) scores on WOMAC total score was significant (β = −0.191, P <0.001) suggesting PEI-2 was a moderator. Simple slope analyses showed that the relationship between PRS and WOMAC was stronger for participants with a low level of PEI-2 (gradient 3.056) than for those with a high level of PEI-2 (gradient 1.746). Conclusion Patient enablement moderated the impact of pain on HRQoL. A higher level of enablement can lessen impairment in HRQoL associated with chronic back and knee pain.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".