Barriers and facilitators to reducing paracetamol use in low back pain: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Paracetamol is widely used for low back pain (LBP), but research questions its efficacy and safety. Patient education booklets have been explored for promoting deprescribing, but barriers and facilitators specific to LBP deprescribing remain unexamined. OBJECTIVE: To identify contextual factors facilitating and obstructing successful deprescribing of paracetamol for LBP after receiving an educational booklet. STUDY DESIGN: This study is part of an uncontrolled cohort feasibility study (CEASE NOW) in the community, recruiting from Musculoskeletal Australia and painaustralia. PATIENT SAMPLE: Twenty-four participants with acute, sub-acute, or chronic LBP, self-reporting paracetamol consumption, were included. METHODS: Thematic content analysis was used to analyze qualitative data on barriers and facilitators. Data were categorized by deprescribing outcomes: i) successful deprescribing, ii) attempted but failed, or iii) no attempt. Semi-structured telephone interviews were conducted within one week after each participant completed the one-month follow-up. RESULTS: Successful deprescribing was facilitated by supportive healthcare professionals, willingness, high self-efficacy, fear of future illness, and diverse strategies for deprescribing plans. Barriers included unsupportive healthcare professionals and fear of flare-ups. Participants not attempting deprescribing believed it unnecessary, perceived it as effortful, unquestioningly trusted healthcare professionals, and lacked risk awareness. CONCLUSIONS: Support from healthcare professionals, patient willingness, perceived necessity, risk awareness, effort, and varied strategies influence deprescribing outcomes for LBP patients using paracetamol. Addressing these factors is crucial when designing interventions to promote safe and effective deprescribing in LBP management.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".