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Record W2066875428 · doi:10.1080/14622200701767787

Smoking during the night: Prevalence and smoker characteristics

2008· article· en· W2066875428 on OpenAlexfundno aff
Deborah M. Scharf, Michael S. Dunbar, Saul Shiffman

Bibliographic record

VenueNicotine & Tobacco Research · 2008
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersNational Institute on Drug AbuseSocial Sciences and Humanities Research Council of CanadaGlaxoSmithKline
KeywordsMedicineNicotineSmoking cessationSmokeNicotine dependenceNicotine replacement therapyNicotine withdrawalNocturnalDemographyEnvironmental healthPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

We report on the smoking patterns and characteristics of individuals who smoke at night. We also explore the relationship between night smoking, nicotine dependence, and cessation outcomes. Participants (N = 691) were heavy smokers enrolled in cessation research clinics. Data were from three studies. Using ecological momentary assessment, participants monitored their smoking (ad libitum, day and night) on electronic diaries (EDs) during a 2-week baseline period and for 4 weeks following a target quit day. A total of 41% of smokers recorded at least one episode of night smoking. Within this group, night smoking occurred on 26% of nights, averaging two episodes per night. ED data correlated with a single self-report item assessing the frequency of night smoking. Night smoking was associated with greater nicotine dependence and daily caffeine consumption. It also predicted risk for lapsing beyond traditional measures of nicotine dependence. Night smoking is common, is associated with nicotine dependence, and it represents additional risk for cessation failure. People who smoke at night may need nicotine replacement therapy overnight. Future research should determine whether treatments that improve sleep quality also improve cessation outcomes in night smokers.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.366
Teacher spread0.277 · 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 teacher head, 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

Citations48
Published2008
Admission routes1
Has abstractyes

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