Addressing Variability in Drug Quality: Finding The Right âQualityâ Framework(s)
Bibliographic record
Abstract
Background: In many countries, a significant proportion of medicines traded and consumed are of poor or variable quality. Meanwhile, failures in appropriately framing and responding to the problem have led to a proliferation of public health and governance challenges.\nObjective: To examine the issues exacerbating the trade and consumption of medicines of poor or variable quality, as well as present locally relevant strategies.\nMethods: Analytic triangulation was applied to the synthesis of publicly available documents.\nResults: Where economic and regulatory environments are less structured, supply chain security strategies that fixate on ‘counterfeits’ often fail in limiting the prevalence of poor quality medicines. In addition to a multivariate drug quality classification chart, three quality frameworks are presented for examining appropriate policy strategies in mediating drug quality.\nConclusion: These tools can assist stakeholders in determining more locally relevant and context-specific strategies, while interrogating the proposition for greater transparency vis-à-vis drug quality.
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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 | 0.087 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".