Bibliographic record
Abstract
The economic evidence indicates that pharmacotherapy (PT)-based smoking cessation interventions are likely to offer good value from public healthcare investment. Even with low rates of long-term abstinence, PT therapy is among the most cost-effective cancer control interventions available. The arguments for providing universal access to PT are strong, however perhaps not as important as ensuring that PT and/or other smoking cessation interventions and services are used by people who need them. In 2018, nearly every Canadian has access to one 12-week course of PT per year; however, this is not enough to maximize the public health benefits that could be gained from smoking cessation interventions. The evidence show that program participation and long-term abstinence rates can be maximized by including behavior-based supports, individualized program invitations, and adapting programs to local or disadvantaged context. Long-term abstinence and participation rates are the greatest drivers of cost-effectiveness. Improving these outcomes, therefore, is the best way to maximize public investment in smoking cessation. Canadian policy needs to consider this evidence in the design and implementation of programs and to collect health information data on a national level to track success rates.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
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".