The effects of the primary health care providers’ prescription behavior interventions to improve the rational use of antibiotics: a systematic review
Why this work is in the frame
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Bibliographic record
Abstract
Background: Irrational antibiotics use in clinical prescription, especially in primary health care (PHC) is accelerating the spread of antibiotics resistance (ABR) around the world. It may be greatly useful to improve the rational use of antibiotics by effectively intervening providers' prescription behaviors in PHC. This study aimed to systematically review the interventions targeted to providers' prescription behaviors in PHC and its' effects on improving the rational use of antibiotics. Methods: The literatures were searched in Ovid Medline, Web of Science, PubMed, Cochrane Library, and two Chinese databases with a time limit from January 1st, 1998 to December 1st, 2018. The articles included in our review were randomized control trial, controlled before-and-after studies and interrupted time series, and the main outcomes measured in these articles were providers' prescription behaviors. The Cochrane Collaboration criteria were used to assess the risk of bias of the studies by two reviewers. Narrative analysis was performed to analyze the effect size of interventions. Results: A total of 4422 studies were identified in this study and 17 of them were included in the review. Among 17 included studies, 13 studies were conducted in the Europe or in the United States, and the rest were conducted in low-income and-middle-income countries (LMICs). According to the Cochrane Collaboration criteria, 12 studies had high risk of bias and 5 studies had medium risk of bias. There was moderate-strength evidence that interventions targeted to improve the providers' prescription behaviors in PHC decreased the antibiotics prescribing and improved the rational use of antibiotics. Conclusions: Interventions targeted PHC providers' prescription behaviours could be an effective way to decrease the use of antibiotics in PHC and to promote the rational use of antibiotics. However, we cannot compare the effects between different interventions because of heterogeneity of interventions and outcome measures.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it