A Study on the Factors Affecting the Prescription of Injection Medicines in Iran: A Policy Making Approach
Bibliographic record
Abstract
BACKGROUND & AIM: Inappropriate prescribing injection medicines can reduce the quality of medical care, patient safety, and leads to a waste of resources. Sufficient evidence is not available in developing countries to persuade policy-makers to promote rational drug prescription. The objective of this study is to assess some factors affecting the prescription of the injection medicines in Iran. METHODS: In this descriptive-analytic study, the data of 91,994,667 selected prescription letters were collected by the Ministry of the Health and Medical Education (MOHME) throughout the country at the year 2011 which were analyzed through a logarithmic regression model. RESULTS: Results of the study show that the percentage of the prescription letters containing injection items varied from 27 percent (in Yazd) to 57 percent (in Ilam). Also the impact of price on the prescription of the injection medicines was not significant (P=0.55). But the impact of the prescription of antibiotics and corticosteroid on injections were significant (P>0.05) and equal 0.44 and 0.65 respectively. CONCLUSION: Increasing price of injection medicines as a policy towards reducing consumptions cannot be a successful policy. But reducing the use of antibiotics and corticosteroids can be a more effective policy to reduce the use of injection medicines.
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".