Transparency in Nigeria's public pharmaceutical sector: perceptions from policy makers
Bibliographic record
Abstract
BACKGROUND: Pharmaceuticals are an integral component of health care systems worldwide, thus, regulatory weaknesses in governance of the pharmaceutical system negatively impact health outcomes especially in developing countries 1. Nigeria is one of a number of countries whose pharmaceutical system has been impacted by corruption and has struggled to curtail the production and trafficking of substandard drugs. In 2001, the National Agency for Food and Drug Administration and Control (NAFDAC) underwent an organizational restructuring resulting in reforms to reduce counterfeit drugs and better regulate pharmaceuticals 2. Despite these changes, there is still room for improvement. This study assessed the perceived level of transparency and potential vulnerability to corruption that exists in four essential areas of Nigeria's pharmaceutical sector: registration, procurement, inspection (divided into inspection of ports and of establishments), and distribution. METHODS: Standardized questionnaires were adapted from the World Health Organization assessment tool and used in semi-structured interviews with key stakeholders in the public and private pharmaceutical system. The responses to the questions were tallied and converted to scores on a numerical scale where lower scores suggested greater vulnerability to corruption and higher scores suggested lower vulnerability. RESULTS: The overall score for Nigeria's pharmaceutical system was 7.4 out of 10, indicating a system that is marginally vulnerable to corruption. The weakest links were the areas of drug registration and inspection of ports. Analysis of the qualitative results revealed that the perceived level of corruption did not always match the qualitative evidence. CONCLUSION: Despite the many reported reforms instituted by NAFDAC, the study findings suggest that facets of the pharmaceutical system in Nigeria remain fairly vulnerable to corruption. The most glaring deficiency seems to be the absence of conflict of interest guidelines which, if present and consistently administered, limit the promulgation of corrupt practices. Other major contributing factors are the inconsistency in documentation of procedures, lack of public availability of such documentation, and inadequacies in monitoring and evaluation. What is most critical from this study is the identification of areas that still remain permeable to corruption and, perhaps, where more appropriate checks and balances are needed from the Nigerian government and the international community.
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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 | 0.012 | 0.012 |
| 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 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".