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
The present study investigated the latent topics and language styles present in mental health organizational discourse on Twitter. The researchers sought to analyze identifying the prevalence of and language used in social support messaging in tweets about mental health care, the overarching topics regarding mental health care, and predicted that tweets with higher engagement will have increased frequency of words with positively valenced emotion and cognitive processing. A GSDMM was run to uncover latent themes that emerged in a data set of 326.9k tweets and 7.2 m words about organizational discussions of mental health. A generalized linear model using the Poisson distribution was used to assess the role of engagement, positive emotion, and cognitive processing. The study found support for both positive emotion and cognitive processing as statistically significant predictors of engagement. Directions for research include the development of health message strategies, policy needs, and online interventions.
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Source |
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http://dx.doi.org/10.1080/10810730.2023.2278609 | DOI Listing |
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