Bidirectionality of smoking and depression in adolescents: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Recently, evidence has been accumulating that both smoking and mental health disorders are continuously increasing among adolescents. This systematic review elucidates the research into evidence of the direction of the association and risk factors influencing the relationship between smoking and depression. We also highlight recent studies on the effects of electronic cigarettes and developments on the association between depression and smoking. METHODS: A literature search was conducted on databases including PubMed, Ovid Medline, EMBASE, and PsycINFO and in relevant neurology and psychiatry journals. Terms used for electronic searches included smoking, tobacco, cigarettes; depression; adolescent, youth; direction. Relevant information was then utilized to synthesize findings on the association between smoking and depression among adolescent population. RESULTS: The initial database searches yielded 2,738 related articles. After screening and cross-referencing, duplicate articles, articles published in languages other than English, and studies on animals, social and lifestyle factors, mood disorders, and substance use were excluded. Of these, a total of 122 publications only focusing on smoking and depression in the adolescent population were selected for synthesis in this qualitative systemic review. These include 110 original research articles, eight meta-analyses and reviews, and four reports and websites. CONCLUSION: The relationship between smoking and depression in the literature does not reflect the cause-effect relationship. The lack of evidence on the direction of the association may reflect futile study designs, confounding factors and/or use of indirect measures of depression and quantification of smoking. Future prospective randomized studies should target elucidation of the causal association.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".