Using Data Mining for Rapid Complex Case Study Descriptions: Example of Public Health Briefings During the Onset of the COVID-19 Pandemic
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Abstract
The methodological purpose of this article is to demonstrate how data mining contributes to rapid complex case study descriptions. Our complexity-informed design draws on freely accessible datasets reporting the public health response surrounding the onset of the COVID-19 pandemic in Alberta (Canada) and involves the cross analysis of integrated findings across six periods of fluctuation identified in the initial quantitative phase of a convergent sequential approach. We discuss how our case meta-inferences, informing how public health briefings can build credibility and trust, were derived by attending to three key concepts of complex adaptive systems: emergence, interdependence, and adaptation. This article serves as an essential reference for using data mining within a case study–mixed methods design for studying complex phenomena.
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SciScore for 10.1101/2020.11.11.20229393: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our generation of a holistic complex case description from the cross analysis of the integrated findings at the six key fluctuation periods mitigated the limitations of either qualitative or quantitative approaches alone more efficiently than had we collected the data ourselves or analyzed the entire case dataset. Second, the use of a …
SciScore for 10.1101/2020.11.11.20229393: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our generation of a holistic complex case description from the cross analysis of the integrated findings at the six key fluctuation periods mitigated the limitations of either qualitative or quantitative approaches alone more efficiently than had we collected the data ourselves or analyzed the entire case dataset. Second, the use of a complexity-informed CS-MM design to study complex phenomena for the purpose of generating novel public health insights aligns with one of the four theoretical conceptions of complexity theory for mixed methods researchers advanced by Kallenmeyn and colleagues (2020). Our CS-MM design procedures, informed by complexity theory, provide practical guidance for documenting the emergent, interdependent, and adaptive realities of the initial public health response to the COVID-19 pandemic. Together, these collective insights along with drawing upon the interdisciplinary and diverse methodological expertise of the researchers involved in the current study, we provide an essential reference for others to learn from and build upon. Strengths, Limitations, and Future Directions: Reliance on open access data creates new opportunities for the use of text mining techniques within mixed methods research designs as an efficient means to detect trends within large datasets. While the use of media briefings is unique as a means of accessing the message conveyed by public health authorities, it is also limited because we do not have data about the impacts of the m...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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