Beyond MPOWER: a systematic review of population-level factors that affect European tobacco smoking rates
Bibliographic record
Abstract
BACKGROUND: Population-level factors within and beyond the scope of the World Health Organization's (WHO) MPOWER policy package have significant impacts on smoking rates. However, no synthesis of the existing evidence exists. This systematic review identifies population-level factors that influence cigarette smoking rates in European countries. METHODS: We searched the ProQuest database collection for original, peer-reviewed quantitative evaluations that investigated the effects of population-level exposures on smoking rates in European countries. Of the 3122 studies screened, 62 were ultimately included in the review. A standardized data extraction form was used to identify key characteristics of each study including publication year, years evaluated, countries studied, population characteristics, study design, data sources, analytic methods, exposure studied, relevant covariates and effects on tobacco smoking outcomes. RESULTS: One hundred and fifty-five population-level exposures were extracted from the 62 studies included in the review, 99 of which were related to WHO MPOWER measures. An additional 56 exposures fell into eight policy realms: economic crises, education policy, macro-economic factors, non-MPOWER tobacco regulations, population welfare, public policy, sales to minors and unemployment rates. About one-half of the MPOWER exposures affected smoking rates (55/99) and did so in an overwhelmingly positive way (55/55). Over three-quarters of the non-MPOWER exposures were associated with statistically significant changes in smoking outcomes (43/56), with about two-thirds of these exposures leading to a decrease in smoking (29/43). CONCLUSIONS: Population-level factors that fall outside of the WHO's MPOWER measures are an understudied research area. The impacts of these factors on tobacco control should be considered by policymakers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".