The Effect of Survey Nonresponse on Quitline Abstinence Rates: Implications for Practice
Bibliographic record
Abstract
INTRODUCTION: Quitline outcome studies are used to maintain and improve the effectiveness of these evidence-based cessation services. Nonresponse has the potential to bias survey results and many US and Canadian quitlines are reporting survey response rates below 50%. This study examines the effect of nonresponse bias on quit rates in three state quitline populations. Results provide implications of nonresponse bias for quitline practice. METHODS: Quit status, defined as abstinent for 30 days or more 7 months after registering for services, was collected from Minnesota, Hawaii, and Florida quitline participants that responded to a survey. We assigned each responder to a wave based on the number of contacts required to obtain a survey response. RESULTS: The latest two responder groups had the lowest quit rates within each state, although results were not statistically significant. Quit rates in the latest responder wave (Wave 6) were between 4% and 13% points lower than the earliest responders (Wave 1). The cumulative quit rates show what the quit rate would have been had the study ended after the corresponding wave. In all four studies, the cumulative quit rate was lowest in Wave 6. CONCLUSION: To increase accuracy of quit rates, quitlines should focus on increasing survey response rates. Suggestions for improving survey response rates are provided.
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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 | 0.370 | 0.661 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".