Dimensions underlying legislator support for tobacco control policies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".