Efficacy of digital interventions for smoking prevention among children: a systematic review
Bibliographic record
Abstract
Abstract Background Smoking remains a leading preventable cause of morbidity and mortality globally, with initiation often occurring in childhood. Early smoking onset is linked to increased difficulty in quitting and higher risk of long-term health complications. Traditional prevention strategies have not fully succeeded in curbing youth smoking rates, highlighting the need for innovative approaches. Digital interventions (DI), such as gamified education, social media campaigns, and virtual reality, offer a novel avenue for engagement and education. This systematic review aims to assess the efficacy of DI in preventing smoking, and improving knowledge and awareness about smoking damages, among children. This review is part of the PRIN project, whose goal is to evaluate the effectiveness of a DI aimed at school-aged children, intended to increase knowledge about the negative effects of smoking and prevent its use. Methods On the 18th of March 2024, PubMed, Scopus, and PsycINFO were surfed for trials and observational studies evaluating DI for smoking prevention among children aged 6-12 years. Study quality will be evaluated using the Cochrane Risk of Bias Tool and New Castle-Ottawa scale. The protocol has been registered in PROSPERO. Results From 3,081 initially identified articles, 328 papers were removed because duplicate. The screening process is ongoing. Conclusions DI are a promising tool for engaging young populations through platforms they are familiar with and receptive to. By leveraging the high penetration of digital technology in the daily lives of children, these interventions have the potential to deliver smoking prevention messages in a compelling and interactive manner. Understanding the effectiveness of DI in smoking prevention can significantly contribute to public health by providing viable alternatives to traditional methods and potentially reducing future smoking-related health burdens. Key messages • Evaluating digital strategies for smoking prevention among children could offer vital alternatives to traditional methods, potentially reducing long-term health risks. • Digital interventions (DI) offer promising new ways to engage young audiences on smoking prevention using familiar platforms, potentially reshaping public health approaches.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.006 | 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".