Anticounterfeiting strategies of local drug manufacturers in lagos, nigeria: drug safety and implications for public health
Bibliographic record
Abstract
BACKGROUND: Nigeria has been plagued by counterfeit and poor quality medicines with several studies indicating varying degree of prevalence. Thus, this study is aimed at determining the anticounterfeiting strategies employed by local drug manufacturers in Lagos-Nigeria. METHOD: The first phase was a descriptive study which involves the use of a self-administered closed ended structured questionnaire to assess the anticounterfeiting strategies employed by local manufacturers in Nigeria. The second phase was an experimental study which involves selection of two classes of most frequently faked drugs identified by the respondents in the first phase. The selected drugs were then subjected to spot checks using the truscan analysis deployed by NAFDAC to identify counterfeit medicine. Anticounterfeiting features on the samples were also examined. The data obtained from phase one were analyzed using SPSS and the data obtained from phase two were entered into the truscan data sheet and analyzed using Chi-squared and ANOVA. Results were considered to be significant at P-value ≤ 0.05. RESULTS: The outcome of the study showed that 83% and 78% of antimalarials drawn from the manufacturing sources and open market respectively passed the truscan spot checks. Similarly, 50% of antibiotics drawn from the two sampling sites passed the truscan checks. There was no significant difference (p ≥ 0.05) between the sampled antimalarials and antibiotics from the manufacturing sources and open market. CONCLUSION: The current rate of counterfeit medicines is relatively low. Strategies to encourage the use of combination of anticounterfeiting technologies by the manufacturers should be established.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".