Evaluation of the effect of informing patients through text messaging on antibiotic prescription by physicians in outpatient setting: a study protocol
Bibliographic record
Abstract
Introduction: Irrational prescription of antibiotics has become a major global concern, and not only does it have health-related consequences, but it also affects countries’ overall economy. Based on reports and studies, antibiotics are prescribed in approximately 50% of prescriptions in Iran which can demand by patients as a major cause. It is anticipated that increasing the awareness and understanding of both physicians and patients, regarding the antibiotic use and resistance, could play an important role in the rational prescription of antibiotic medications. In this study, we will examine the effect of informing patients via text message right before their appointment on the proportion of prescribed antibiotic medications.Material and Methods: In this study, a randomized control trial (RCT) will be conducted. The setting in which the study will be carry out, consists of 64 physicians (29 general physician and 35 specialist). Unit of randomization will be physicians based on the proportion of their prescriptions that include antibiotic medications (PIA). The first arm of the study is the intervention group, which consists of the patients receiving three text messages in the clinic’s waiting rooms. The second arm is the control group, and consists of the patients who won’t be receiving any text messages. The content of the text messages focuses on the consequences of self-medication with antibiotics, the fact that the use of antibiotics is not an option for curing viral diseases including cold, and it also asks the patients not to demand antibiotics by trusting their physicians.Results: The main variable that will be measured is the proportion of prescriptions that include antibiotic medications.Conclusion: This trial will be the first one to evaluate the patients’ role in the proportion of prescriptions that include antibiotic medications. It is hypothesized that patients’ demand for antibiotic medication is one of the main causes of irrational antibiotic prescription by physicians.
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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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".