Daily smoking patterns, their determinants, and implications for quitting.
Bibliographic record
Abstract
In this article, the authors examine daily temporal patterns of smoking in relation to environmental restrictions on smoking and cessation outcomes. Time-series methods were used for analyzing cycles in 351 smokers who monitored their smoking in real time for 2 weeks. The waking day was divided into 8 "bins" of approximately 2 hr, cigarette counts were tallied for each bin, and temporal patterns of smoking and restriction were analyzed. Cluster analyses of smoking patterns by time of day resulted in 4 clusters: daily decline (n = 30; 9%), morning high (n = 43; 12%), flatline (n = 247; 70%), and daily dip-evening incline (n = 31; 9%). Clusters differed in baseline demographic, smoking, and psychosocial variables. Results suggest that smoking behavior can be characterized by regular patterns of smoking frequency during the waking day: Smoking in the flatline cluster was within +/-0.5 standard deviation at all times. For the other clusters, smoking was high in the morning (daily dip-evening incline: +1.7 standard deviations; morning high: +2.8 standard deviations; daily decline: +1.7 standard deviations); moderate (morning high: -0.8 standard deviations; daily decline: +0.3 standard deviations) or low (daily dip-evening incline: -1.0 standard deviations) midday; and high (daily dip-evening incline: +2.0 standard deviations), moderate (morning high: +0.5 standard deviations), or low (daily decline: -1.5 standard deviations) in the evening. Daily smoking patterns were related to environmental smoking restrictions, but the strength of this relationship differed among clusters and by time of day. Clusters differed in lapse risk.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".