Assessment of validity of self-reported smoking status. Health Rep. 2012, 23, 47–53. © 2014 by the authors; licensee MDPI
Bibliographic record
Abstract
Background Cigarette smoking is associated with adverse health effects, including cancer, respiratory illness, heart disease and stroke. National data on smoking prevalence often rely on self-reports. This study assesses the validity of self-reported cigarette smoking status among Canadians. Data and methods Data are from the 2007 to 2009 Canadian Health Measures Survey, a nationally representative cross-sectional survey of 4,530 Canadians aged 12 to 79. The survey included self-reported smoking status and a measure of urinary cotinine, a biomarker of exposure to tobacco smoke. The prevalence of cigarette smoking was calculated based on self-reports and also on urinary cotinine concentrations. Results Compared with estimates based on urinary cotinine concentration, smoking prevalence based on self-report was 0.3 percentage points lower. Sensitivity estimates (the percentage of respondents who reported being smokers among those classifi ed as smokers based on cotinine concentrations) were similar for males and females (more than 90%). Although sensitivity tended to be lower for respondents aged 12 to 19 than for those aged 20 to 79, the difference did not attain statistical signifi cance. Interpretation Accurate estimates of the prevalence of cigarette smoking among Canadians can be derived from self-reported smoking status data.
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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 | 0.029 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".