Smoking and psychological health in relation to country of origin
Bibliographic record
Abstract
In English-speaking, Western-Anglo countries, where smoking has become stigmatized in recent decades as a result of widespread anti-smoking campaigns, smokers commonly report poorer psychological health on average than nonsmokers do. This may be indirectly related to the strong pressures to quit in such countries, as poorer psychological health is associated with a reduced likelihood of quitting, thus leading to a selection bias for smokers with relatively poorer psychological health. In the present study, 147 smoker and nonsmoker participants either came from Western-Anglo countries where smoking has become stigmatized (Australia, Canada, USA) or countries in regions where smoking remains relatively more accepted (Asia, Latin America, Europe). Smokers and nonsmokers were assessed on a widely used self-report measure of anxiety, depression, and stress. Multivariate analysis revealed a significant interaction between smoker status (smoker, nonsmoker) and country of origin (Western-Anglo, other) on psychological health ratings, with univariate analysis showing a significant interaction on anxiety scores. Among those from Western-Anglo countries, smokers reported significantly higher levels of anxiety than nonsmokers did, whereas there was no difference in anxiety between smokers and nonsmokers from other countries. There was no difference in the number of cigarettes smoked per day between the samples of smokers, indicating very similar levels of nicotine intake in the two groups. The findings support the notion that a selection bias for smokers with relatively poorer psychological health is occurring in Western-Anglo countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".