Encouraging pharmaceutical innovation to meet the needs of both developed and developing countries
Bibliographic record
Abstract
Purpose Current pharmaceutical global pricing strategies functionally exclude developing countries from the market for drugs to treat many diseases. The purpose of this paper is to evaluate some of the proposed patent reward models to determine their feasibility in the current environment. Design/methodology/approach A review of a variety of proposals including special financing or tax arrangements, public‐private partnerships, and government‐funded patent purchases are briefly reviewed. A more in‐depth examination of the recently proposed health impact fund (HIF) is undertaken. Findings In brief, the HIF requires developed countries to donate to a fund that finances the release of pharmaceutical patents. The pharmaceutical companies would be reimbursed over a ten‐year period from the government donation pool based on the medicine's health impact. The expected consequence of this policy would be affordable medicines for developed and developing countries. This examination highlights deficiencies in the current HIF strategy and offers a number of suggestions mostly focused on a more balanced sharing of the inherent risks in pharmaceutical product development to improve the strategies viability. Practical implications Although among the proposed strategies, the HIF offers the most promise, the suggested changes would result in a program viewed more favourably by the pharmaceutical industry and participating countries. Originality/value Although it is recognized that pricing challenges are limiting the availability to essential medications in developing countries, current strategies often ignore many of the market dynamics for pharmaceuticals. This critique, focused on the HIF strategy, is presented in an effort to improve the likely success of the most promising of these strategies.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".