scholarly journals Quality Management in Libraries Case Study: Book Collection Retrieval in Academic Libraries

2021 ◽  
Vol 9 (2) ◽  
pp. 182
Author(s):  
Vilianty Rizki Utami ◽  
Desni Sri Hastuti Sihite
Keyword(s):  
Quality Control ◽  
Quality Management ◽  
Comprehensive Evaluation ◽  
Case Study Method ◽  
Control Data ◽  
User Expectations ◽  
And Performance ◽  
Quality Control Data

Quality management in the library is related to quality control of the library's work to meet user expectations and contributes to the continued success of the organization. However, quality control is lacking and not be the main focus in many libraries. This paper aims to explain how quality management improves work results in libraries. The research method used in this paper is qualitative research using a case study method. We conduct research in Library X that already underwent quality control in book processing activities. The data were collected through observation, and interviews for book processing activities and its quality control data during 2018-2020. The data was then analyzed document analysis. The study found that Library X could perform better by improving the quality of book processing and fixing the error just before they put the book on its shelves. Quality control gives a comprehensive evaluation in Library X either for humans, processes, and systems of book processing activities that help Library X conduct its duty to provide their user needs and expectations. Quality control and quality management also help Library X describe the library working atmosphere and can be used for giving motivation to all librarians and staff to give better service and performance for the end-users.

Assessing the influence of component processing and donor characteristics on quality of red cell concentrates using quality control data

Vox Sanguinis ◽  
2016 ◽  
Vol 111 (1) ◽  
pp. 8-15 ◽  
Author(s):  
A. Jordan ◽  
D. Chen ◽  
Q. -L. Yi ◽  
T. Kanias ◽  
M. T. Gladwin ◽  
...  
Keyword(s):  
Quality Control ◽  
Red Cell ◽  
Control Data ◽  
Donor Characteristics ◽  
Quality Control Data

scholarly journals Evaluation of Quality Control Data of Clinical Chemistry Parameters using Six Sigma Metrics Tool in Clinical Laboratory

2021 ◽  
pp. 305-309
Author(s):  
G. Anuradha ◽  
S. Santhinigopalakrishnan ◽  
S. Sumathy
Keyword(s):  
Quality Control ◽  
Six Sigma ◽  
Clinical Laboratory ◽  
Clinical Biochemistry ◽  
Clinical Chemistry ◽  
Control Data ◽  
Laboratory Performance ◽  
Clinical Biochemistry Laboratory ◽  
Quality Control Data

Background: Physicians rely on laboratory results for treating patients. So it is the duty of laboratories to assure quality of the results released. So laboratory performance should be validated to maintain the quality. Six sigma has now gained recent interest in monitoring the laboratory quality.This study was designed to gauge the clinical chemistry parameters based on six sigma metrics. Materials and Methods: In this retrospective study, both the internal and external quality control data of 26 clinical chemistry parameters were collected for a period of 6 months from June 2020 to November 2020 and the six sigma analysis was done at the Central clinical biochemistry laboratory of Chettinad Hospital and research institute. Results: AST, amylase, lipase, triglyceride, HDL, iron, magnesium, creatine kinase showed sigma values more than 6.Uric acid, total protein, ALT, direct bilirubin, GGT,cholesterol, cholesterol, calcium, TIBC and phosphorus shows sigma values between 3.5 to 6. Glucose, BUN, creatinine, albumin, Na, K, Chloride, showed sigma values less than 3.5. Conclusion: Six sigma metrics can help in improving the quality of laboratory performance and also helps to standardisethe actual amount of QC that is required by the laboratory for maintaining quality.

System errors of manufacturers of medicines detected by conducting of state control in the area of circulation of medicines

2019 ◽  
Vol 2019 (5) ◽  
pp. 32-38
Author(s):  
Валентина Косенко ◽  
Valentina Kosenko ◽  
Алла Трапкова ◽  
Alla Trapkova ◽  
Светлана Тарасова ◽  
...  
Keyword(s):  
Quality Control ◽  
Quality Management ◽  
Management Systems ◽  
State Control ◽  
Quality Management Systems ◽  
Conducting State ◽  
Pharmaceutical Quality ◽  
State Quality ◽  
System Errors

The article conducts the analysis of system errors detected by Roszdravnadzor by conducting state quality control of circulating medicines, as well as weaknesses in pharmaceutical quality management systems of the manufacturers, that can influence the quality of manufactured drugs.

scholarly journals Popularity and performance of bioinformatics software: the case of gene set analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chengshu Xie ◽  
Shaurya Jauhari ◽  
Antonio Mora
Keyword(s):  
Comprehensive Evaluation ◽  
Gene Set Analysis ◽  
Tool Selection ◽  
Functional Interpretation ◽  
Selection Procedures ◽  
Gene Set ◽  
And Performance ◽  
Reported Data ◽  
Performance Results

Abstract Background Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies. Results Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods (“GSA-BenchmarKING”). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods. Conclusions The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.

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