Smoking-Cessation Advice from Health-Care Providers - Canada, 2005
Bibliographic record
Abstract
Tobacco use is the most preventable cause of premature death and disease in Canada. In 2002, an estimated 37,209 Canadians died from illnesses related to tobacco use, accounting for 16.6% of all deaths in Canada. One of the objectives of the Canadian Federal Tobacco Control Strategy (FTCS) 2001-2011 is to reduce smoking prevalence in Canada from 25% to 20%. Although evidence indicates that an effective and efficient way of providing smoking-cessation information to smokers is through contact with health-care providers, little data in Canada exist regarding smoking-cessation advice from this group. In 2005, the Canadian Tobacco Use Monitoring Survey (CTUMS) included questions to assess self-reported provision of cessation advice by health-care providers. This report summarizes the results of that survey, which indicate that only half of persons who visited health-care providers in the preceding 12 months received smoking-cessation advice, suggesting that health-care providers need to take greater advantage of opportunities to provide such advice to smokers.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".