A Comparative Study on Different Pharmaceutical Industries and Proposing a Model for the Context of Iran.
Bibliographic record
Abstract
Medication is known as the main and the most effective factor in improving public health. On the other hand, having a strong and effective pharmaceutical industry will, to a very large extent, guarantee people's health. Therefore, this study was prospected to review the different pharmaceutical industries around the world and propose a model for the context of Iran. This is a qualitative as well as a comparative study which was carried out in 2015. At the first stage, using the World Bank website, countries were divided into four groups of low-income, lower-middle-income, upper-middle-income, and high-income economies. Then, four countries of Afghanistan, India, Brazil, and Canada were chosen from these four groups, respectively. Secondly, data gathered from these countries were given to two 12-member expert panels. Finally, using the articles and the results of expert panel groups, useful and effective policies were extracted for the growth and development of Iran's pharmaceutical industry. Findings of the study indicated that the following seven items are the essential policies for the context of Iran: establishment of high academic centers as well as research institutes, using weak patent law, supporting research and development centers at universities and pharmaceutical companies, backing national pharmaceutical companies up, implementing generic rules, gradual economic liberalization, and membership in world trade organization. Since, pharmaceutical industry is an effective and inseparable part of every health system, proper and evidence-based policies should be taken into account in order to develop this industry and, ultimately, meet the public needs.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".