Comparison of the registration process of the medicines control authority of Zimbabwe with Australia, Canada, Singapore, and Switzerland: benchmarking best practices
Bibliographic record
Abstract
Benchmarking regulatory systems of low- and middle-income countries with mature systems provides an opportunity to identify gaps, enhance review quality, and reduce registration timelines, thereby improving patients’ access to medicines. The aim of this study was to compare the medicines registration process of the Medicines Control Authority of Zimbabwe (MCAZ) with the regulatory processes in Australia, Canada, Singapore, and Switzerland. A questionnaire that standardizes the review process, allowing key milestones, activities and practices of the five regulatory authorities was completed by a senior member of the divisions responsible for issuing marketing authorizations. The MCAZ has far fewer resources than the regulatory authorities in the comparator countries, but employs three review models, which is in line with international best practice. The MCAZ registration process is similar to the comparator countries in key milestones monitored, but differs in the target timelines for these milestones. The MCAZ is comparable to the comparator authorities in implementing the majority of good review practices, although it significantly lags behind in transparency and communication. This study identified the MCAZ strengths and opportunities for improvement, which if implemented, will enable the achievement of its vision to be a leading regulatory authority in Africa.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".