Daily smoking patterns, their determinants, and implications for quitting.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it