Understanding factors influencing antibiotic prescribing behaviour in rural China: a qualitative process evaluation of a cluster randomized controlled trial
Bibliographic record
Abstract
Objectives We conducted a qualitative process evaluation embedded in a cluster randomized controlled trial in rural Guangxi China, which successfully reduced antibiotic use for children upper respiratory tract infections. This study aims to report on the factors that influenced behaviour change among providers and caregivers in the intervention arm, and to explore contextual considerations which may have influenced trial outcomes. Methods A total of 35 in-depth interviews were carried out with hospital directors, doctors, and caregivers of children. Participants were recruited from six purposively selected facilities, including two higher performing and two lower performing facilities per trial results. Interviews were conducted in Chinese and translated to English. We also observed guideline training sessions and prescription peer review meetings. Data were analysed using framework analysis. Results Intervention-arm doctors described that training sessions improved their knowledge, skills and confidence in appropriate prescribing. This was contrasted by control arm participants who did not receive training and reported less agency in reducing prescribing rates. Prescription peer review meetings were seen as an opportunity for further education, action planning and goal setting, particularly in high performing hospitals, where these meetings were led by senior doctors who were perceived to have relevant clinical experience. Caregiver participants reported that intervention educational materials were helpful but they identified information from doctors was more useful. Providers and caregivers also described contextual health system factors, including hospital competition, short consultation times, and antibiotic availability without prescription, which shaped care preferences. Conclusions This qualitative process evaluation identified a range of factors that may have influenced behaviour among providers and caregivers leading to observed changes in reducing inappropriate antibiotic prescribing in China. Future interventions to reduce antibiotic prescribing should consider system level and wider contextual factors to better understand behaviours and patient care preferences.
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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 | 0.196 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".