Brief education on chronic low back pain: Brief group education for patients with chronic low back pain – a descriptive study
Bibliographic record
Abstract
ObjectiveSome studies show that patient education involving self management strategies can be a good way to address the negative impact of psychosocial factors in chronic low back pain (CLBP). The purpose of this study was to describe the clinical changes following a 2.5-hour group education session for patients with non-specific CLBP.MethodsForty-two patients participated in this study; 20 received a 2.5-hour group education session and 22 were assigned to a waiting list. The education session addressed strategic issues in CLBP such as false beliefs, fear-avoidance beliefs, exercises and pain management strategies. The outcomes were pain intensity, pain catastrophizing, pain coping strategies, self-perceived functional status and mood. Data were collected before the education session and 3 months after.ResultsThree months after the education session, the intervention group showed a clinically significant reduction in the perceived pain intensity and pain catastrophizing thoughts; however, the intra- and inter-group analysis revealed no statistically significant differences in any outcome measures.ConclusionAlthough brief group education could be an interesting stand alone therapeutic intervention for patients with CLBP, more research is required to better study the type, dose and effectiveness of group education in CLBP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".