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Sex differences in nicotine dependence among addictions clients accessing a smoking cessation programme in Vancouver, British Columbia, Canada

2011· article· en· W1942078252 on OpenAlexafffundabout
Chizimuzo T.C. Okoli, Iris Torchalla, Milan Khara

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2011
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsVancouver Coastal HealthCentre for Advancing Health Outcomes
FundersCanadian Institutes of Health ResearchHealth Canada
KeywordsPsychosocialAddictionMedicineAnxietyPsychiatryNicotineSmoking cessationMoodNicotine dependenceSubstance abuseDemographyClinical psychology

Abstract

fetched live from OpenAlex

Accessible summary The purpose of this study was to examine differences in substance use disorders, psychiatric disorders and nicotine dependence among 323 women and men accessing a smoking cessation programme in an addiction treatment setting in Vancouver, British Columbia, Canada. Individuals with substance use and psychiatric disorders have smoking prevalence rates nearly double that of the general population. Yet, there are distinct differences between men and women in their smoking behaviour and responses to smoking cessation treatment. Few studies have examined such sex differences among individuals with substance use and psychiatric disorders. The study found that compared with individuals with no psychiatric diagnosis, those with a mood, anxiety and psychotic disorders were significantly more likely to be female; whereas compared with those without a substance use disorder, individuals with alcohol, cocaine or marijuana disorder were more likely to be male. Moreover, among women having an anxiety disorder history and smoking a greater number of cigarettes per day were significantly associated with high nicotine dependence. Among men, smoking a greater number of cigarettes per day and having a lower confidence in quitting were significantly associated with high nicotine dependence. These findings suggest the need for appropriate assessment of smoking behaviour and nicotine dependence among individuals accessing addictions treatment services. Moreover, these findings further provide evidence of the need for tailored interventions for tobacco dependence among men and women with histories of substance use and psychiatric disorder. Abstract Most individuals in drug treatment programmes use tobacco and are dependent on nicotine. For 323 participants (65% men, mean age = 49.3 years) with a history of substance use disorder (SUD) and/or psychiatric disorders (PD) enrolled in a tobacco dependence clinic programme, we compared baseline characteristics among women and men and examined factors associated with nicotine dependence (ND). Individuals with mood, anxiety and psychotic disorders were more likely to be female, whereas men were more likely to be characterized by alcohol, cocaine and marijuana use, older age, older age at smoking initiation and higher confidence in quitting smoking scores. In stratified multivariate analyses, among women, history of an anxiety disorder and a greater number of cigarettes smoked per day were associated with higher ND scores; among men, a greater number of cigarettes smoked per day and higher confidence in quitting scores were associated with higher ND scores. Given the differences in smoking, SUD and PD histories between women and men accessing addiction treatment, and differential associations with ND, it is important to further explore factors that may enhance tailored treatments and inform future studies examining biological and psychosocial factors for tobacco use in SUD and PD treatment populations.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.298
Teacher spread0.269 · 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

Citations5
Published2011
Admission routes3
Has abstractyes

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