Can rational prescribing be improved by an outcome-based educational approach? A randomized trial completed in Iran
Bibliographic record
Abstract
INTRODUCTION: An outcome-based education approach has been proposed to develop more effective continuing medical education (CME) programs. We have used this approach in developing an outcome-based educational intervention for general physicians working in primary care (GPs) and evaluated its effectiveness compared with a concurrent CME program in the field of rational prescribing. METHODS: A cluster randomized controlled design was used. All 159 GPs working in 6 cities, in 2 regions in East Azerbaijan province in Iran, were invited to participate. The cities were matched and randomly divided into an intervention arm, for an outcome-based education on rational prescribing, and a control arm for a traditional CME program on the same topic. GPs' prescribing behavior was assessed 9 months before, and 3 months after the CME programs. RESULTS: In total, 112 GPs participated. The GPs in the intervention arm significantly reduced the total number of prescribed drugs and the number of injections per prescription. The GPs in the intervention arm also increased their compliance with specific requirements for a correct prescription, such as explanation of specific time and manner of intake and precautions necessary when using drugs, with significant intervention effects of 13, 36, and 42 percentage units, respectively. Compared with the control arm, there was no significant improvement when prescribing antibiotics and anti-inflammatory agents. DISCUSSION: Rational prescribing improved in some of the important outcome-based indicators, but several indicators were still suboptimal. The introduction of an outcome-based approach in CME seems promising when creating programs to improve GPs' prescribing behavior.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".