The Impact of Asking About Interest in Free Nicotine Patches on Smoker’s Stated Intent to Change: Real Effect or Artefact of Question Ordering?
Bibliographic record
Abstract
INTRODUCTION: Stage of change questions are often included on general population surveys to assess the proportion of current smokers intending to quit. The current study reported on a methodological experiment to establish whether participant's self-reported stage of change can be influenced by asking about interest in free nicotine patches immediately prior to asking about intent to change. METHODS: As part of an ongoing random digit dialing survey, a randomized half of participants were asked if they would be interested in receiving nicotine patches to help them quit smoking prior to being asked whether they intended to quit smoking in the next 6 months and 30 days. RESULTS: Participants who were first asked about interest in free nicotine patches were more likely to rate themselves as in preparation for change (asked first = 33%; not asked first = 19%), and less likely to rate themselves as in the precontemplation stage of change (asked first = 34%; not asked first = 47%), compared with participants who were not asked about their interest in free nicotine patches prior to being asked about their stage of change (P < .001). CONCLUSIONS: There are several possible explanations of the results. It is possible that offers of free nicotine patches increases smokers intentions to quit, at least temporarily. Alternatively, smokers being asked about interest in free nicotine patches may expect that the researchers would like to hear about people intending to quit, and respond accordingly.
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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 | 0.149 | 0.357 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".