The make or buy debate: Considering the limitations of domestic production in Tanzania
Bibliographic record
Abstract
BACKGROUND: In order to ensure their population's regular access to essential medicines, many least developed countries and developing countries are faced with the policy question of whether to import or manufacture drugs locally, in particular for life-saving antiretroviral medicines for HIV/AIDS patients. In order for domestic manufacturing to be viable and cost-effective, the local industry must be able to compete with international suppliers of medicines by producing sufficiently low cost ARVs. METHODS: This paper considers the 'make-or-buy' dilemma by using Tanzania as a case study. Key informant interviews, event-driven observation, and purposive sampling of documents were used to evaluate the case study. The case study focused on Tanzania's imitation technology transfer agreement to locally manufacture a first-line ARV (3TC + d4T + NVP), reverse engineering the ARV. RESULTS: Tanzania is limited by weak political support for the use of TRIPS flexibilities, limited production capacity for ARVs and limited competitiveness in both domestic and regional markets. The Ministry of Health and Social Welfare encourages the use of flexibilities while others push for increased IP protection. Insufficient production capacity and lack of access to donor-financed tenders make it difficult to obtain economies of scale and provide competitive prices. CONCLUSIONS: Within the "make-or-buy" context, it was determined that there are significant limitations in domestic manufacturing for developing countries. The case study highlights the difficulty of governments to make use of economies of scale and produce low-cost medicines, attract technology transfer, and utilize the flexibilities of the WTO Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS). The results demonstrate the importance of evaluating barriers to the use of TRIPS flexibilities and long-term planning across sectors in future technology transfer and manufacturing initiatives.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".