Study on the implementation of reverse logistics in medicines from health centers in Brazil
Bibliographic record
Abstract
The need to implement innovative sustainable production processes is a topic of important social relevance in this current century. Environmental tragedies on different continents have revealed that humanity needs to understand how nature belongs to a system that needs to be balanced in order to maintain its existence. One of the major sustainability challenges globally is the management of solid wastes (SW). The rapid urbanization coupled with the increase in the world population has resulted in a consequential increase in the use of various products and their associated generated wastes. Of such SW that is complicated to manage are medical wastes which are deemed hazardous. Thus, the various industries including the health industry must implement a reverse logistic methodology in the disposal of solid wastes in order to efficiently and effectively manage their wastes. Countries such as Brazil, have incorporated reverse logistics into their National Solid Waste Policy (NSWP) thereby ensuring adequate disposal of various solid wastes and co-responsibility among the generators of various wastes. In this study, the strategies adopted within the scope of the Unified Health System (SUS, in Portuguese) in Brazil related to the reverse logistics of medicines consumed in the health industry were investigated. As a methodology, qualitative research such as literature review and documentary were utilized in order to obtain knowledge about the major information about the health industry in Brazil. The findings and discussions provided in this paper revealed that reverse logistics applied to pharmaceutical products in Brazil is still incipient. Although there is consolidated literature on the subject in national studies, there is a lack of evidence that indicates effective models of reverse drug logistics in the country, in a scenario of growth of 10% per year in the generation of this waste on average in the country. It is concluded that reverse drug logistics in Brazil is in an embryonic process, strengthened by the construction of regulations and laws that may encourage industries to adopt good practices in the production and management of drug wastes, and which, when implemented, can generate a reduction of around 12% in the volume generated per year.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".