The freeze on mass media campaigns in England: a natural experiment of the impact of tobacco control campaigns on quitting behaviour
Bibliographic record
Abstract
AIMS: To measure the impact of the suspension of tobacco control mass media campaigns in England in April 2010 on measures of smoking cessation behaviour. DESIGN: Interrupted time series design using routinely collected population-level data. Analysis of use of a range of types of smoking cessation support using segmented negative binomial regression. SETTING: England. MEASUREMENTS: Use of non-intensive support: monthly calls to the National Health Service (NHS) quitline (April 2005-September 2011), text requests for quit support packs (December 2007-10) and web hits on the national smoking cessation website (January 2009-March 2011). Use of intensive cessation support: quarterly data on the number of people setting a quit date and 4-week quitters at the NHS Stop Smoking Services (SSS) (quarter 1, 2001 and quarter 3, 2011). FINDINGS: During the suspension of tobacco control mass media spending, literature requests fell by 98% [95% confidence interval (CI) = 96-99], and quitline calls and web hits fell by 65% (95% CI = 43-79) and 34% (95% CI: 11-50), respectively. The number of people setting a quit date and 4-week quitters at the SSS increased throughout the study period. CONCLUSIONS: The suspension of tobacco control mass media campaigns in England in 2012 appeared to markedly reduce the use of smoking cessation literature, quitline calls and hits on the national smoking cessation website, but did not affect attendance at the Stop Smoking Services. Within a comprehensive tobacco control programme, mass media campaigns can play an important role in maximizing quitting activity.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".