Encouraging the use of generic medicines: implications for transition economies.
Bibliographic record
Abstract
Generic drugs have a key role to play in the efficient allocation of financial resources for pharmaceutical medicines. Policies implemented in the countries with a high rate of generic drug use, such as Canada, Denmark, Germany, the Netherlands, the United Kingdom, and the United States, are reviewed, with consideration of the market structures that facilitate strong competition. Savings in these countries are realized through increases in the volume of generic drugs used and the frequently significant differences in the price between generic medicines and branded originator medicines. Their policy tools include the mix of supply-side measures and demand-side measures that are relevant for generic promotion and higher generic use. On the supply-side, key policy measures include generic drug marketing regulation that facilitates market entry soon after patent expiration, reference pricing, the pricing of branded originator products, and the degree of price competition in pharmaceutical markets. On the demand-side, measures typically encompass influencing prescribing and dispensing patterns as well as introducing a co-payment structure for consumers/patients that takes into consideration the difference in cost between branded and generic medicines. Quality of generic medicines is a pre-condition for all other measures discussed to take effect. The paper concludes by offering a list of policy options for decision-makers in Central and Eastern European economies in transition.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".