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Record W4417296765 · doi:10.1093/pch/pxaf116.058

58 A digital screening and brief intervention tool to help parents and adolescents quit smoking and vaping: Feasibility results from a pilot randomized-controlled trial

2025· article· en· W4417296765 on OpenAlexaffabout
Émile Diamant, Nicholas Chadi, Tamara Pérez, Cristal Namuhoranye, Olivier Drouin

Bibliographic record

VenuePaediatrics & Child Health · 2025
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineMcGill University Health CentreUniversité de Montréal
Fundersnot available
KeywordsIntervention (counseling)Smoking cessationRandomized controlled trialPilot trialBrief interventionPrimary careQuit smokingPsychological intervention

Abstract

fetched live from OpenAlex

Abstract Background Tobacco use is the single greatest preventable cause of mortality in Canada and 15% of Canadian children are exposed to household secondhand smoke. Clinical Effort Against Secondhand Smoke (CEASE) is an effective, cost-efficient intervention for parents accompanying children to medical appointments that offers systematic screening and facilitated access to evidence-based tobacco cessation resources. Objectives This study is a single-blind pilot randomized controlled trial, comparing an adapted version of CEASE (CanCEASE) to usual care in 5 general and specialized paediatric clinics in Montreal. Design/Methods Parents of children 0-17 years old and adolescents 14-17 years old were included if they reported using cigarettes or e-cigarettes in the past 7 days. Data regarding smoking and vaping consumption were collected at baseline and at 1-, 3- and 6-month follow-up through a web-based survey. Our primary outcome was the number of participants recruited per recruitment day. Secondary outcomes included proportion of individuals screened eligible for participation, interest in smoking/vaping cessation and participant retention. Results Among the 280 adolescents and 568 parents screened, 53 adolescents and 74 parents were recruited (recruitment rate: 0.56/day for adolescents and 0.78/day for parents). Most participants were white (87% of adolescents and 85% of parents) and identified as female (55% of adolescents and 73% of parents); 44.6% of parents had not completed post-secondary education. Observed clinic-level vaping and smoking prevalence were 20% (95%CI 15.6-25.3) and 7.9% (95%CI 5.1-11.8) among adolescents and 8.1% (95%CI 6.1-10.7) and 14.6% (95%CI 11.9-17.9) among parents, respectively. More than half of parents who smoked cigarettes, and more than 40% of parents who used e-cigarettes expressed high (≥ 8/10) intention to quit. Retention rates at 6 months were 54.7% (95%CI 40.6-68.2) for adolescents and 51.4% (95%CI 39.5-63) for parents. Among all participants who completed the 6-month questionnaire, interest in obtaining behavioural cessation support and nicotine-replacement-therapy were 7.7% (95%CI 1.3–26.6) and 3.8% (95%CI 0.2–21.6) among adolescents, and 16.1% (95%CI 6.1–34.5) and 22.6% (95%CI 10.3–41.5) among parents, respectively. Conclusion We conclude that screening parents and adolescents for smoking and vaping and delivering an electronic cessation assistance intervention in paediatric clinic waiting rooms was feasible. Our results reveal high interest in quitting smoking and vaping among parent participants, suggesting an important opportunity to offer cessation support in such settings. Next steps include a fully powered randomized-control trial testing the effectiveness of CanCEASE and the development of future interventions targeting adolescents who smoke and use e-cigarettes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.029
GPT teacher head0.323
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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