Are Questions from the Italian National Health Survey Adequate to Measure Prevalence of Smoking Among Teens
Bibliographic record
Abstract
OBJECTIVES: Studies on the prevalence of smoking among Italian adolescents have generated inconsistent estimates. Notably, the Italian National Health Survey (INHS) generates relatively lower estimates than estimates reported in other studies. The INHS asks adults and adolescents if they are smokers or nonsmokers. Research has shown that adolescent smoking is unstable compared to that of adults, and that adolescents may acquire their identity as smokers only after smoking becomes more established. We hypothesized that the INHS prevalence estimates of adolescent smoking could be improved by adding questions on smoking behavior. METHODS: During the school year 1993-1994, 471 participants responded to a brief survey on smoking experiences. We compared the prevalence of smoking behavior with the prevalence of smoking identity of participants (mean age = 16.18) who attended five high schools in two Northern Italian cities, Padova and Bergamo. RESULTS: Measures of smoking behavior generated higher prevalence estimates than did measures of identity, particularly among occasional smokers. CONCLUSIONS: The INHS should add behavioral measures of smoking to maximize the accuracy of prevalence estimates.
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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".