Effectiveness of doctors’ advice on non-prescription antibiotic use: a randomized controlled trial, China
Bibliographic record
Abstract
Objective: To evaluate a family doctor-led, community-based intervention to reduce non-prescription antibiotic use. Methods: We conducted a parallel-group, cluster-randomized controlled trial at 22 community health centres in Shenzhen, China, over an 8-month period in 2023. We randomly (1 : 1) assigned community health centres to provide a 4-week, family doctor-led, community-based online health intervention, or to provide routine care only. Eligible participants were adults aged 18 to 75 years who had resided in the community for more than 6 months. The primary outcome was the level of non-prescription antibiotic use (including self-medication with antibiotics and purchase of antibiotics without a prescription). Secondary outcomes were: levels of self-medication with antibiotics; purchase of antibiotics without a prescription; self-storage of antibiotics; and prescribed antibiotic use. Findings: We enrolled 1550 participants, with 788 assigned to the intervention group and 762 to the control group. We observed a significant decrease in non-prescription antibiotic use in the intervention group compared to the control group (odds ratio, OR: 0.49; 95% confidence interval, CI: 0.31-0.77) at 6 months. There was a significant reduction in self-medication (OR: 0.33; 95% CI: 0.13-0.83) and purchase of antibiotics without a prescription (OR: 0.59; 95% CI: 0.37-0.94), but not in self-storage (OR: 0.80; 95% CI: 0.54-1.18) or prescribed antibiotic use (OR: 0.94; 95% CI: 0.48-1.87) at 6 months. Conclusion: The family doctor-led, community-based intervention demonstrated promising effectiveness and feasibility. This study provides valuable insights for the design and implementation of such interventions aimed at promoting rational use of antibiotics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".