The Reach Ratio--A New Indicator for Comparing Quitline Reach Into Smoking Subgroups
Bibliographic record
Abstract
INTRODUCTION: There is growing concern about population disparities in tobacco-related morbidity and mortality. This paper introduces the reach ratio as a complementary measure to reach for monitoring whether quitline interventions are reaching high risk groups of smokers proportionate to their prevalence in the population. METHODS: Data on smokers were collected at intake by 7 Canadian provincial quitlines from 2007 to 2009 and grouped to identify 4 high risk subgroups: males, young adults, heavy smokers, and those with low education. Provincial data are from the Canadian Tobacco Use Monitoring Survey. Reach ratios (ReRas), defined as the proportion of quitline callers from a subgroup divided by the proportion of the smoking population in the subgroup, and 95% confidence intervals were calculated for the subgroups. A ReRa of 1.0 indicates proportionate representation. RESULTS: ReRas for male smokers and young adults are consistently less than 1.0 across all provinces, indicating that a lower proportion of these high-risk smokers were receiving evidence-based smoking cessation treatment from quitlines. Those with high levels of tobacco addiction and less than high school education have ReRas greater than 1.0, indicating that a greater proportion of these smokers were receiving cessation treatments. CONCLUSION: ReRas complement other measures of reach and provide a standardized estimate of the extent to which subgroups of interest are benefiting from available cessation interventions. This information can help quitline operators, funders, and policymakers determine the need for promotional strategies targeted to high risk subgroups, and allocate resources to meet program and policy objectives.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".