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Record W3138734326 · doi:10.1016/j.pmedr.2021.101363

Smoking cessation or initiation: The paradox of vaping

2021· article· en· W3138734326 on OpenAlexaff
Mohammed Al‐Hamdani, Eden Manly

Bibliographic record

VenuePreventive Medicine Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsDalhousie UniversitySaint Mary's University
Fundersnot available
KeywordsLegalizationUnintended consequencesSmoking cessationAddictionAction (physics)MedicineYoung adultTobacco controlEnvironmental healthPsychologyPsychiatryPublic healthGerontologyPolitical science

Abstract

fetched live from OpenAlex

In recent years, there has been a rapid expansion of the vaping market which has led many to question whether vaping can assist people in smoking cessation, or if it in fact paves the way for new smokers. While there has not been conclusive evidence regarding vaping as a smoking cessation tool, there is striking evidence that vaping is linked to new smoking addictions, especially in teenagers and young adults. Despite the prevalent belief that tobacco is more harmful to the body, early research on vaping has already shown very detrimental effects, and the comprehensive effects may become much clearer in the years to come. To curtail the rapidly increasing number of teenagers and young adults vaping, strict action must be taken. Legalization with tight control of vaping products would focus the efforts on those attempting to quit, while helping to prevent acquisition by teenagers and young adults that are not of legal age. In the years to come, vaping controls should be carefully considered to ensure that the purported benefits of helping those overcome a smoking addiction are not outweighed by the unintended consequences of creating a teenage demographic addicted to vaping.

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)0.005
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0080.001

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 head0.063
GPT teacher head0.354
Teacher spread0.291 · 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 designObservational
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

Citations31
Published2021
Admission routes1
Has abstractyes

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