The effectiveness of the Swiss and Canadian antibiotic distribution system compared
Bibliographic record
Abstract
The aim of research paper was to explore the current antibiotic distribution system in Switzerland, as well as all the important players of this complex industry. In order to better understand how this industry functions. It is important to get a big picture by understanding the implication of pharmaceutical companies, health insurance companies, hospitals, doctors, pharmacies and drugstores, and finally the role of the government and other higher instances. The pharmaceutical world is a highly regulated and complex environment and to better understand how the pharmaceutical industry functions, it was important to understand the regulations and guidelines to follow. Furthermore, Canada was use throughout this paper as a comparison. The aim was to understand the Canadian key players, how their system functions. The global health care market was an estimated worth of more than US$ 8’452 billion in 2018 and is dominated by few key players such as Novartis, Roche, Pfizer (Businesswire 2019; Mikulic 2020). These key players are responsible for medication supply throughout the world, including antibiotics. Antibiotics have revolutionized society, when discovered in the early 20s, by Alexander Fleming. They have since helped fight several deadly infections. Antibiotics were a great contribution to the medical world; however, bacteria have, over the years, started to show resistance to certain antibiotic treatment. Scientifics and medical professionals are raising alarm bells to tackle antimicrobial resistance (AMR). The Swiss antibiotic distribution system is highly regulated and conducted in two different ways. One part of Switzerland dispenses antibiotics solely through doctors, the other part only through pharmacies, whereas 2 cantons are exceptions, and have a dual distribution system. Furthermore, an estimate 30 percent of total medication is wasted yearly in Switzerland (Tornare 2017). The Swiss health sector is seeing its prices and costs rise year over year and inefficiency arise. Politicians try to address the inefficiency problem, but their means are only limited. This paper intends to give a clear overview of the complex Swiss health care system. It will also compare the Swiss health care system and its key stakeholders with Canada to understand what is done differently and where there could be improvements. A combination of primary and secondary data will be used to address the effectiveness of the Swiss and Canadian antibiotics distribution system.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".