Is the smoking-depression relationship confounded by alcohol consumption? An analysis by gender
Bibliographic record
Abstract
There is a well-established relationship between cigarette smoking and depression. The purpose of the current study is to examine whether alcohol use may be a confounder in this relationship, and whether this relationship differs between men and women. As part of a national survey, 14,063 Canadians were interviewed using random-digit dialing and computer-assisted telephone interviewing. Responses to questions on smoking during the past 12 months allowed participants to be classified as never, former, light, mid-level, and heavier smokers. Alcohol use measures included: usual frequency, usual quantity per drinking occasion, heavy episodic drinking (5 or more drinks), and hazardous drinking. Depression was measured as (a) meeting clinical diagnostic criteria for depression (Composite International Diagnostic Interview) and (b) recent depressed affect (Center for Epidemiological Studies of Depression scale). Multinomial logistic regressions indicated that the association between smoking and depression was only slightly reduced and remained significant when drinking status and drinking pattern were controlled for. The relationship between smoking and depression was stronger for women when depression was measured as meeting clinical criteria for depression, with all categories of smoking by women but only mid-level and heavier smoking by men significant related to depression.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".