Target quit date timing as a predictor of smoking cessation outcomes.
Bibliographic record
Abstract
Evidence is mixed on whether timing of a target quit date (TQD) has an effect on quit success. The purpose of this secondary analysis of data from a prospective longitudinal study was to determine if time to TQD was a predictor of smoking abstinence at follow-up. Between 2011 and 2013, a total of 5,793 adult smokers participated in a 1-hr psychoeducation workshop and received 5 weeks of nicotine patch treatment. All participants were required to indicate a TQD within 1 month of the workshop. Latency to TQD was categorized into quartiles: 0 to 1 day (first quartile: 28.1%); 2 to 6 days (second quartile: 22.4%); 7 to 19 days (third quartile: 25.4%); 20-31 days (fourth quartile: 24.0%). Compared with participants who chose an immediate TQD within 1 day of the workshop, odds of having quit smoking at end-of-treatment and 6-month follow-up did not significantly differ among those who set a TQD within 2-6 days (5-weeks: adjusted odds ratio [AOR] = 0.89, p = .315; 6-months: AOR = 0.89, p = .417), but were significantly lower for those who chose a TQD either 7-19 days (5-weeks: AOR = 0.76, p = .023; 6-months: AOR = 0.70, p = .013) or 20-31 days (5-weeks: AOR = 0.64, p = .001; 6-months: AOR = 0.69, p = .017) after the workshop. TQD timing was an independent predictor of smoking cessation outcomes after controlling for potential confounding variables including confidence in quitting ability, importance of quitting, nicotine dependence, and number of nicotine patches used. (PsycINFO Database Record
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".