(C)onsumer focused (E)ducation on p(A)racetamol (S)ide (E)ffects, i(N)adequate (O)utcomes and (W)eaning (CEASE NOW) for individuals with low back pain: results of a feasibility study
Bibliographic record
Abstract
OBJECTIVES: To determine the feasibility of a patient-education booklet to support patients with low back pain (LBP) to reduce paracetamol intake. DESIGN: Single group, repeated measures feasibility study. SETTING: Community. PARTICIPANTS: Adults experiencing LBP of any kind and self-reporting consumption of paracetamol for LBP weekly for at least 1 month were invited to participate. INTERVENTION: Participants received a patient-education booklet 1 week after the baseline measures were collected. The intervention was designed to change beliefs, increase knowledge and self-efficacy to deprescribe paracetamol for their LBP and create discussion with a health professional through the mechanisms of motivation, capacity and opportunity. PRIMARY OUTCOME MEASURES: Feasibility of recruitment procedures, data collection and acceptability of the intervention. SECONDARY OUTCOME MEASURES: Changes in motivation, self-efficacy, opportunity to deprescribe paracetamol for their LBP, paracetamol usage and LBP clinical outcomes at baseline, 1-week and 1-month follow-up. RESULTS: A total of 24 participants were recruited into the study within the timeframe of 3 months from study advertisement and all completed the study follow-up. There were no missing data for any outcome measure across all follow-up points, 22 (91.6%) participants were willing to participate in a future randomised control trial (RCT) and over 60% of participants responded positively to questions regarding acceptability of the patient-educational booklet. Overall, at the 1-month follow-up, approximately two thirds (15/24) of participants had an increase in motivation and self-efficacy scores and had discussed or intended to discuss their paracetamol use for LBP with a health professional. CONCLUSIONS: The results of this study demonstrate that the patient-education booklet is feasible to implement, and both the intervention and study design were well-received by participants. This study supports the undertaking an RCT to assess the effects of the patient-education booklet on deprescribing paracetamol in people with LBP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".