Factors in Nonadherence to Quitline Services
Bibliographic record
Abstract
BACKGROUND: Quitlines offer evidence-based, multisession coaching support for smoking cessation in the 50 U.S. states, Canada, and several other countries. Smokers who enroll in quitline services have, ipso facto, shown readiness to attempt to quit, but noncompletion of coaching services appears widespread and has not been widely investigated. The current study explored the magnitude and correlates of quitline service abandonment. METHOD: A state's quitline intake, coaching, and nicotine patch/gum utilization data were obtained for smokers who enrolled during the period July 2007 to June 2008 (n = 20,882). Analyses examined demographic, socioeconomic status, nicotine dependence-related, and nicotine replacement therapy--utilization factors associated with completion of only one coaching session (of five offered). RESULTS: Almost half of enrollees (47.8%) completed only one session. All significant predictors together explained less than 4% of variance; not being sent nicotine replacement therapy was most strongly correlated with completion of only one session. A framework is proposed for directing research toward reducing quitline service nonadherence. CONCLUSIONS: Premature user abandonment of coaching calls is widespread within a quitline. Further research should determine the extent of the problem in national quitline systems, increase knowledge of mediators of nonadherence, and develop strategies for increasing coaching completion.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".