Reported planning before and after quitting and quit success: Retrospective data from the ITC 4-Country Survey.
Bibliographic record
Abstract
Planning before quitting smoking is widely believed to be beneficial and is usually recommended in cessation counseling, but there is little evidence on the efficacy of specific planning activities. Using data from 1140 respondents who reported quit attempts at Wave 8 of the ITC 4-Country Survey, we analyzed use of 8 specific planning strategies before (5) and after (3) implementation of a quit attempt, in relation to cessation outcomes, delay in implementation of the attempt, and recent quitting history. Most participants reported some planning both before and after quitting, even among those reporting quitting 'spontaneously.' Younger smokers, those who cut down before quitting, and users of stop-smoking medication were more likely to report planning. Those who planned prequit were also more likely to plan postquit. Unexpectedly, we found no clear benefit of planning on short-term (1 month) cessation outcomes, whereas one prequit strategy (practicing not smoking) was negatively related to outcome. There was evidence for a predicted moderating effect of recent quitting experience on planning for the prequit task 'practice replacement strategies.' This predicted quit success among those with multiple quit attempts in the past year, but failure among those without. This finding suggests that the quality of planning may be critical. More research, particularly on the moderating effect of quit experience, and where measures of planning are collected before outcomes become evident, is needed before clear recommendations can be made on the utility of various forms of planning for the success of quit attempts.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".