OBJECTIVE: To propose and test a new classification system for characterising legislator support for various tobacco control policies. DESIGN: Cross sectional study. SUBJECTS: Federal and provincial legislators in Canada serving as of October 1996 who participated in the Canadian Legislator Study (n = 553; response rate 54%). MAIN OUTCOME MEASURES: A three factor model (Voters, Tobacco industry, Other interest groups) that assigns nine tobacco control policies according to legislators' hypothesised perceptions of which group is more directly affected by these policies. RESULTS: Based on confirmatory factor analysis, the proposed model had an acceptable fit and showed construct validity. Multivariate analysis indicated that three of the predictors (believing that the government has a role in health promotion, being a non-smoker, and knowledge that there are more tobacco than alcohol caused deaths) were associated with all three factor scales. Several variables were associated with two of the three scales. Some were unique to each scale. CONCLUSIONS: Based on our analyses, legislator support for tobacco control policies can be grouped according to our a priori factor model. The information gained from this work can help advocates understand how legislators think about different types of tobacco control policies. This could lead to the development of more effective advocacy strategies.
OBJECTIVE: To propose and test a new classification system for characterising legislator support for various tobacco control policies. DESIGN: Cross sectional study. SUBJECTS: Federal and provincial legislators in Canada serving as of October 1996 who participated in the Canadian Legislator Study (n = 553; response rate 54%). MAIN OUTCOME MEASURES: A three factor model (Voters, Tobacco industry, Other interest groups) that assigns nine tobacco control policies according to legislators' hypothesised perceptions of which group is more directly affected by these policies. RESULTS: Based on confirmatory factor analysis, the proposed model had an acceptable fit and showed construct validity. Multivariate analysis indicated that three of the predictors (believing that the government has a role in health promotion, being a non-smoker, and knowledge that there are more tobacco than alcohol caused deaths) were associated with all three factor scales. Several variables were associated with two of the three scales. Some were unique to each scale. CONCLUSIONS: Based on our analyses, legislator support for tobacco control policies can be grouped according to our a priori factor model. The information gained from this work can help advocates understand how legislators think about different types of tobacco control policies. This could lead to the development of more effective advocacy strategies.
Authors: R G Laforge; W F Velicer; D A Levesque; J L Fava; D J Hill; P E Schofield; D Fan; H De Vries; W O Shisana; M Conner Journal: Tob Control Date: 1998 Impact factor: 7.552
Authors: B S Flynn; A O Goldstein; L J Solomon; K E Bauman; N H Gottlieb; J E Cohen; M C Munger; G S Dana Journal: Prev Med Date: 1998 Mar-Apr Impact factor: 4.018
Authors: Jonathan Purtle; Félice Lê-Scherban; X I Wang; Paul T Shattuck; Enola K Proctor; Ross C Brownson Journal: Milbank Q Date: 2019-11-11 Impact factor: 4.911
Authors: Meagan Pilar; Jonathan Purtle; Byron J Powell; Stephanie Mazzucca; Amy A Eyler; Ross C Brownson Journal: Community Ment Health J Date: 2022-06-11