The impact of the COVID-19 pandemic on smoking in adolescents: a scoping review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic and associated policies have influenced adolescent smoking behaviours, with potential impacts on smoking initiation, cessation, and addiction. This scoping review aims to summarise existing evidence on how the pandemic affected adolescent smoking behaviour across various contexts. METHODS: A systematic search was conducted in September 2023 across three databases-Embase, APA PsycInfo, and Medline-using terms related to adolescents, COVID-19 exposure, and smoking behaviours. Studies were included if they focused on adolescents aged 12-21, examined smoking-related outcomes during or after the pandemic, and were published from 2019 onwards. Study quality was not assessed in this research. The search identified 18 studies, which were independently screened by two reviewers, with conflicts resolved by a third reviewer. Thematic analysis was used to categorise the studies. RESULTS: Of the 18 studies, most were retrospective and focused on high-income countries, including the United States, Israel, and the Netherlands. Trends in smoking behaviour varied, with some studies reporting increased smoking during the pandemic, particularly in regions like the United States and Netherlands; others observed reductions in smoking, such as in France and Spain; and others observed mixed results, such as South Korea. The impact of mental health was significant, with increased anxiety and depression linked to higher smoking rates, especially in the United States and Israel. Several known risk factors, such as peer influence, parental smoking habits, and family dynamics, also played a role. Reduced peer interactions and time spent with family were associated with reductions in smoking behaviour. In contrast, adverse family dynamics or the presence of smoking family members contributed to higher smoking rates. Further, the impact of COVID-19 on these factors varied: peer influence decreased due to social distancing measures, while mental health issues such as increased anxiety and depression were associated with higher smoking rates. CONCLUSION: This review highlights the complex and heterogeneous impacts of COVID-19 on adolescent smoking behaviours. Mental health, social interactions, and family dynamics were key factors influencing smoking patterns. These findings can inform the development of targeted smoking cessation and prevention strategies for adolescents, particularly in the context of future public health crises.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".