Objectives: The aim of the study was to analyse the proportion of evidence-based medication displayed in pharmacies and compare it between the different linguistic regions of the country, at different times of the year to determine the amount of proven effective medications indirectly recommended to the public in different parts of Switzerland.
Design: This is an observational study conducted by medical doctors in the department of internal medicine at the Spitalzentrum Biel, Switzerland.
Setting: The observation took place from July 2019 to May 2020. From a total of 1800 pharmacies in Switzerland, 68 different pharmacies were selected across the 3 main linguistic regions and the medication on display in their windows were examined 4 times a year regarding their efficacy. The displays of medication with or without evidence-based efficacy were described using absolute numbers and proportions and compared between the different linguistic regions at different seasons using χ.
Participants: There were no human or animal participants involved in this study.
Primary And Secondary Outcome Measures: The primary outcome is the proportion of medication displayed in pharmacy windows with a proven effectiveness in medical literature. The secondary outcome was the variability of the primary outcome over time (seasonal changes), over the different linguistic regions of Switzerland and between chains and privately owned pharmacies.
Results: We examined 970 medications and found that over the whole year, there is a high proportion of non-evidence-based drugs (56,9%) displayed in pharmacies. Swiss German cantons display significantly more non-evidence-based medications in winter. We found no statistical difference for other seasons or between chains and privately owned pharmacies.
Conclusion: Pharmacies in Switzerland tend to display significantly more non-evidence-based drugs, thus indirectly recommending them to the public. In a time of necessary expansion of self-medication by the population, this could incite consumers to buy drugs without proven effectiveness.
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http://dx.doi.org/10.1136/bmjopen-2022-069186 | DOI Listing |
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Sungkyunkwan University, Seoul, Republic of Korea.
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PLoS One
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School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom.
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Miin Wu School of Computing, National Cheng Kung University, Tainan, Taiwan.
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View Article and Find Full Text PDFAlzheimers Dement
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Miin Wu School of Computing, National Cheng Kung University, Tainan, Taiwan.
Background: Continuous speech analysis is considered as an efficient and convenient approach for early detection of Alzheimer's Disease (AD). However, the traditional approach generally requires human transcribers to transcribe audio data accurately. This study applied automatic speech recognition (ASR) in conjunction with natural language processing (NLP) techniques to automatically extract linguistic features from Chinese speech data.
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Background: Automated speech and language analysis (ASLA) represents a powerful innovation for detecting and monitoring persons with or at risk for dementia. Given its cost-efficiency and automaticity, its impact can be vital for under-resourced communities, such Spanish-speaking Latinos. However, ASLA markers are understudied in this group and may differ from those established in widely studied populations (e.
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