PP071 Topic: AS09–Global Health/Resource Limited Setting/Health Inequalities/Impact of Global Warming/Other: EXPLORING THE PROBLEM OF SUBSTANDARD AND FALSIFIED DRUGS IN HOSPITAL SETTINGS WORLDWIDE
Bibliographic record
Abstract
Aims & Objectives: Substandard and falsified (SF) drugs still do not receive enough attention in public health as, due to the underground nature of the counterfeit drug business, they are hard to detect. Yet, more than 10% of drugs in low- and middle-income countries (LMIC’s) are estimated be SF (World Health Organization, 2017). These drugs could be harmful to patients, contribute to the progression of antimicrobial resistance, lead to additional care and thus increased out-of-pocket expenditures, burden on health care providers, and loss of confidence in the health system. We aim to understand health workers’ knowledge and perceptions on SF drugs in hospital settings worldwide. Methods: We will take advantage of three large research network (Global Parity, WFPICCS, and LaRED) to send out an online survey to all available health workers in LMIC’s. We will gather information on socio-demographics, knowledge and experience with SF drugs, and perceptions around SF drugs. We will also collect information from the health workers in supervisory roles on general hospital operations and inventory, procurement behavior, and their actions taken to prevent SF drugs to reach patients. Results: We will generate novel descriptive evidence across more than [X] sites across Africa, Latin America, Southeast Asia and the Middle East. Conclusions: SF drugs can potentially be an important contributor to the high mortality rates from sepsis in LMIC’s. This study will inform future interventions to address this issue at the hospital level. Keywords: Drugs, Counterfeit, medications, Sub-standard
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.110 | 0.018 |
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".