Randomized Trial: Quitline Specialist Training in Gain-Framed vs Standard-Care Messages for Smoking Cessation
Bibliographic record
Abstract
BACKGROUND: Smoking accounts for a large proportion of cancer-related mortality, creating a need for better smoking cessation efforts. We investigated whether gain-framed messages (ie, presenting benefits of quitting) will be a more persuasive method to encourage smoking cessation than standard-care messages (ie, presenting both costs of smoking [loss-framed] and benefits of quitting). METHODS: Twenty-eight specialists working at the New York State Smokers' Quitline (a free telephone-based smoking cessation service) were randomly assigned to provide gain-framed or standard-care counseling and print materials. Smokers (n = 2032) who called the New York State Smokers' Quitline between March 10, 2008, and June 13, 2008, were exposed to either gain-framed (n = 810) or standard-care (n = 1222) messages, and all medically eligible callers received nicotine replacement therapy. A subset of 400 call recordings was coded to assess treatment fidelity. All treated smokers were contacted for 2-week and 3-month follow-up interviews. All statistical tests were two-sided. RESULTS: Specialists providing gain-framed counseling used gain-framed statements statistically significantly more frequently than those providing standard-care counseling as assessed with frequency ratings for the two types of gain-framed statements, achieving benefits and avoiding negative consequences (for achieving benefits, gain-framed mean frequency rating = 3.9 vs standard-care mean frequency rating = 1.4; mean difference = -2.5; 95% confidence interval [CI] = -2.8 to -2.3; P < .001; for avoiding negative consequences, gain-framed mean frequency rating = 1.5 vs standard-card mean frequency rating = 1.0; mean difference = -0.5; 95% CI = -0.6 to -0.3; P < .001). Gain-framed counseling was associated with a statistically significantly higher rate of abstinence at the 2-week follow-up (ie, 99 [23.3%] of the 424 in the gain-framed group vs 76 [12.6%] of the 603 in the standard-care group, P < .001) but not at the 3-month follow-up (ie, 148 [28.4%] of the 522 in the gain-framed group vs 202 [26.6%] of the 760 in the standard-care group, P = .48). CONCLUSIONS: Quitline specialists can be trained to provide gain-framed counseling with good fidelity. Also, gain-framed messages appear to be somewhat more persuasive than standard-care messages in promoting early success in smoking cessation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".