Quitting activity and tobacco brand switching: findings from the ITC‐4 Country Survey
Bibliographic record
Abstract
OBJECTIVE: Among Australian smokers, to examine associations between cigarette brand switching, quitting activity and possible causal directions by lagging the relationships in different directions. METHODS: Current smokers from nine waves (2002 to early 2012) of the ITC-4 Country Survey Australian dataset were surveyed. Measures were brand switching, both brand family and product type (roll-your-own versus factory-made cigarettes) reported in adjacent waves, interest in quitting, recent quit attempts, and one month sustained abstinence. RESULTS: Switching at one interval was unrelated to concurrent quit interest. Quit interest predicted switching at the following interval, but the effect disappeared once subsequent quit attempts were controlled for. Recent quit attempts more strongly predicted switching at concurrent (OR 1.34, 95%CI=1.18-1.52, p<0.001) and subsequent intervals (OR 1.31, 95%CI=1.12-1.53, p=0.001) than switching predicted quit attempts, with greater asymmetry when both types of switching were combined. One month sustained abstinence and switching were unrelated in the same interval; however, after controlling for concurrent switching and excluding type switchers, sustained abstinence predicted lower chance of switching at the following interval (OR=0.66, 95%CI=0.47-0.93, p=0.016). CONCLUSIONS: The asymmetry suggests brand switching does not affect subsequent quitting. IMPLICATIONS: Brand switching does not appear to interfere with quitting.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".