Scoping Review Protocol: Healthcare Providers’ Knowledge Gaps and Educational Needs in Addressing Substandard and Falsified Oncology Drugs
Bibliographic record
Abstract
Substandard and falsified (SF) oncology drugs represent a growing threat to global health, undermining patient safety and treatment outcomes. Cancer therapeutics, due to their high market value, frequent shortages, and complex supply chains, are increasingly targeted by counterfeiters. Despite the scale of the problem, there is limited evidence on oncology healthcare providers’ (HCPs) awareness, knowledge, and capacity to identify, report, and prevent exposure to SF oncology drugs. This review aims to map the extent and nature of existing literature addressing SF oncology drugs, identify knowledge and educational gaps among oncology HCPs, and summarize evidence-based interventions that could enhance clinical education and patient safety.
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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 | 0.093 | 0.190 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.154 | 0.029 |
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".