Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Researchers elicit knowledge related to expert decision-making processes to inform information technology design and related interventions. However, in healthcare, many subject matter experts have limited time for such endeavors. In addition, researchers need to analyze voluminous amounts of qualitative data. Thus, we present a data collection and validation methodology: an initial focus group followed by targeted member checking, both supported by data visualization. We ground the work in a homecare admission case study. We conducted a focus group with six homecare admitting nurses at a rural agency. Our custom visualizations of the qualitative results helped to identify potential missing information. We conducted a member checking session with five nurses to validate the focus group results and to address the missing data. The member checking results were incorporated into the custom visualizations. The data collection and validation methodology shows promise for knowledge elicitation in time-constrained situations.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371347 | PMC |
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