Quit4hlth: a preliminary investigation of tobacco treatment with gain-framed and loss-framed text messages for quitline callers
Bibliographic record
Abstract
Abstract Introduction Recent evidence suggests that quitline text messaging is an effective treatment for smoking cessation, but little is known about the relative effectiveness of the message content. Aims A pilot study of the effects of gain-framed (GF; focused on the benefits of quitting) versus loss-framed (LF; focused on the costs of continued smoking) text messages among smokers contacting a quitline. Methods Participants were randomized to receive LF (N = 300) or GF (N = 300) text messages for 30 weeks. Self-reported 7-day point prevalence abstinence and number of 24 h quit attempts were assessed at week 30. Intent-to-treat (ITT) and responder analyses for smoking cessation were conducted using logistic regression. Results The ITT analysis showed 17% of the GF group quit smoking compared to 15% in the LF group (P = 0.508). The responder analysis showed 44% of the GF group quit smoking compared to 35% in the LF group (P = 0.154). More participants in the GF group reported making a 24 h quit attempt compared to the LF group (98% vs. 93%, P = 0.046). Conclusions Although there were no differences in abstinence rates between groups at the week 30 follow-up, participants in the GF group made more quit attempts than those in the LF group.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".