Evaluating the quality of data in articles by faculty members of the Faculty of Pharmacy, Kerman University of Medical Sciences, based on the DQA model

Given that the production of scientific articles in medical departments in Iran is relatively high and many of these articles are published in reputable journals, it is necessary to evaluate the articles produced by the faculty members of the Faculty of Pharmacy of Kerman University of Medical Sciences in order to determine the quality of the data used in these articles. This research attempts to evaluate the quality of the data of the articles produced by the faculty members of the Faculty of Pharmacy of Kerman University of Medical Sciences based on the DQA model, while identifying non-standard cases, extract a scientific evaluation result from these produced articles, and by identifying unstructured cases, help produce correct and healthy data in future scientific products. The main objective of this research is to evaluate the quality of the data of the articles produced by the faculty members of the Faculty of Pharmacy of Kerman University of Medical Sciences based on the DQA model. This research is of an applied type, and its method is a descriptive-evaluative survey. The statistical population of this research is 340 articles from the articles of the faculty members of the Faculty of Pharmacy of Kerman University of Medical Sciences in the period of 2018-2022. Based on Morgan's table, 181 articles were randomly selected as a sample. A researcher-made questionnaire based on the DQA model with 6 main criteria (data validity, data reliability, timeliness and up-to-dateness of data, data accuracy, data integrity, and data consistency) with a Cronbach's alpha validity of 0.896 was used to assess the quality of the data of the articles. In this study, the fuzzy Delphi method was used to identify and screen the quality criteria of the articles. The members of the Delphi group were 4 (40%) men and 6 (60%) women. Also, 10% of the research population had the scientific rank of professor, 30% of the expert panel had the scientific rank of assistant professor, and 60% of the expert panel had the scientific rank of associate professor. Based on the results of the Delphi group members, criteria with an average of less than 0.7 were eliminated. To extract data, first, keywords related to the topic of the articles were selected, then using key filters, data were extracted based on research components (data validity, data reliability, timeliness and up-to-dateness of data, data accuracy, data integrity, and data consistency) based on the DQA model. The evaluation of the data quality of the articles was determined based on the Bazargan table scale and one-sample t-test. The research findings indicate that, among the 181 articles from the faculty members of the Faculty of Pharmacy of Kerman University of Medical Sciences that were reviewed, an average of 2 articles (1.2%) were in a completely unfavorable state, 3 articles (1.7%) were in an unfavorable state, 16 articles (8.8%) were in a somewhat favorable state, 130 articles (71.8%) were in a favorable state, and 30 articles (16.5%) were in a completely favorable state. Since the quality of the data of the articles in the desirable and completely desirable category was 88.3%, it shows that the quality of the data of the articles is mostly in a desirable state in terms of data integrity.

As a result, the data validity component with the highest frequency (13) is a completely undesirable option and the integrity component with the lowest frequency (7) is a completely desirable option among the dimensions of the quality of the data of the articles. The data up-to-dateness component with a frequency of (42) and the data compatibility component with a frequency of (40) are completely desirable options have the highest level of quality of the article data.

The results of the main hypothesis test of the research, with an average data quality (µ0 = 3.83), show that the articles are in a desirable state. The results of the main hypothesis test of the research, with an average data quality (µ0 = 3.83), show that the articles are in a desirable state.

The results of the research showed that the evaluation of the data quality of the articles based on the DQA model is in a desirable state. Collecting valid and accurate data from valid, reliable and up-to-date sources, using advanced statistical methods for more accurate data analysis, eliminating invalid data, correcting missing data and controlling human errors, using machine learning algorithms for more accurate data prediction and analysis, using various tests and methods for data validation, and finally, accurate documentation of the stages of data collection, analysis and processing are effective factors in increasing the quality of scientific dat