The role of preexisting analgesic use and self-efficacy for continued use of analgesics among patients with persistent low back pain
Bibliographic record
Abstract
BACKGROUND: Various analgesics are frequently prescribed by physicians and used by patients with low back pain (LBP) despite limited effect on pain and disability and risk of side effects. Current knowledge on how psychological measures and self-management interventions influence analgesic use is limited. We investigated if analgesic use changed after participating in a patient education and exercise therapy program (GLA:D® Back), to what extent analgesic use and self-efficacy at baseline were potential determinants of analgesic use at the end of the program, and, to what extent improvement in self-efficacy from before to after the intervention modified the relationship between analgesic use at baseline and follow-up. METHODS: Back registry collected from March 28, 2018, until October 16, 2023. Potential determinants were self-reported baseline analgesic use and self-efficacy (the Arthritis Self Efficacy Scale pain subscale). The outcome was analgesic use at 3 months follow-up. We used logistic regression to investigate associations and effect modification. RESULTS: Among 4721 included participants, 34% of those using analgesics at baseline (n = 942) discontinued this at 3 months follow-up. Analgesic use at baseline was associated with increased odds of analgesic use at follow-up (odds ratio [OR]: 9.79, 95% confidence interval [CI]: 7.88, 12.15), and higher levels of self-efficacy at baseline was associated with decreased odds of analgesic use at follow-up (OR: 0.85, 95% CI: 0.81, 0.89). Improved self-efficacy, obtained during the program, reduced the risk of analgesic use at follow-up from 15 to 6% and from 76 to 54% among participants with and without baseline analgesic use respectively. CONCLUSIONS: Patients using analgesics when initiating care were more likely to use analgesics three months later, while those having high levels of self-efficacy were less likely. Improved self-efficacy during the program reduced the absolute risk of analgesic use following the intervention to a larger extent among those using analgesics at baseline compared to those without baseline use. Further investigation is needed to confirm whether these findings reflect causal effects.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".