Canadian Tobacco Use Monitoring Survey, 2003: Annual, Person File
Bibliographic record
Abstract
Statistics Canada has conducted smoking surveys on an ad hoc basis on behalf of Health Canada since the 1960s. These surveys have been done as supplements to the Canadian Labour Force Survey and as Random Digit Dialling telephone surveys. In February 1994, a change in legislation was passed which allowed a reduction in cigarette taxes. Since there was no survey data from immediately before this legislative change took place, it was difficult for Health Canada or other interested analysts to measure exactly the impact of the change. As Health Canada wants to be able to monitor the consequences of legislative changes and anti-smoking policies on smoking behaviour, the Canadian Tobacco Use Monitoring Survey was designed to provide Health Canada and its partners/stakeholders with continual and reliable data on tobacco use and related issues. Since 1999, two CTUMS files have been released every year: a file with data collected from February to June and a file with the July to December data. Additionally, there is also a yearly summary. This Public Use Microdata File contains 3 cycles of data including 2 files per cycle. One for Households and one for Persons. Cycle 1: Represent, February to June data Cycle 2: Represent, July to December data Annual: February to December data. This file covers identical questions in both cycle. Geography level of data is Cda, Provincial - Montreal & Toronto were the only two target CMA's The primary objective of the survey is to provide a continuous supply of smoking prevalence data against which changes in prevalence can be monitored. This objective differs from that of the National Population Health Survey (NPHS) which collects smoking data from a longitudinal sample to measure which individuals are changing their smoking behaviour, the possible factors which contribute to change, and the possible risk factors related to starting smoking and smoking duration. Because the NPHS collects data every two years and releases the data about a year after completing the collection cycle, it does not meet Health Canada’s need for continuous coverage in time, rapid delivery of data, or sufficient detail of the most at-risk populations, namely 15-24 year olds. The Canadian Tobacco Use Monitoring Survey allows Health Canada to look at smoking prevalence by province-sex-age group, for age groups 15-19, 20-24, 25-34, 35-44 and 45+ on a semi-annual basis.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.020 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.079 | 0.041 |
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".