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
Objectives: The purpose of this study is to provide an updated profile of gamblers and problem gamblers in Canada and to identify characteristics most strongly associated with problem gambling.
Methods: An assessment of gambling participation and problem gambling was included in the 2018 Canadian Community Health Survey and administered to 23,952 individuals 18 years and older. Descriptive statistics provided a demographic profile for each type of gambling involvement as well as category of gambler (non-gambler, non-problem gambler, at-risk gambler, problem gambler). A logistic regression identified characteristics that best distinguished problem from non-problem gamblers.
Results: Gambling participation and problem gambling both varied as a function of gender, income, educational attainment, and race/ethnicity. However, multivariate analysis identified electronic gambling machine (EGM) participation to be the primary predictor of problem gambling status, with race/ethnicity, presence of a mood disorder, male gender, casino table game participation, older age, a greater level of smoking, participation in speculative financial activity, instant lottery participation, lower household income, and lottery or raffle ticket participation providing additional predictive power. Provincial EGM density and EGM participation rates are also very strong predictors of provincial rates of at-risk and problem gambling.
Conclusion: Problem gambling has a biopsychosocial etiology, determined by personal vulnerability factors combined with the presence of riskier types of gambling such as EGMs. Effective prevention requires a multifaceted approach, but constraints on the availability and operation of EGMs would likely have the greatest single public health benefit.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076356 | PMC |
http://dx.doi.org/10.17269/s41997-020-00443-x | DOI Listing |
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