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Record W4308059828 · doi:10.1007/s10597-022-01041-6

An Examination of the Effectiveness of Smoking Cessation Treatment Interventions for Individuals with Severe Mental Illness: A Pilot Randomized Controlled Trial

2022· article· en· W4308059828 on OpenAlexafffund
Donna Pettey, Jennifer Rae, Tim Aubry

Bibliographic record

VenueCommunity Mental Health Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsCanadian Mental Health AssociationUniversity of Ottawa
FundersMitacs
KeywordsMedicineSmoking cessationPsychosocialRandomized controlled trialMental illnessMotivational interviewingPsychological interventionNicotine replacement therapyMental healthPsychiatryPhysical therapyNicotineInternal medicine

Abstract

fetched live from OpenAlex

To evaluate the effectiveness of two different smoking cessation interventions for individuals with severe mental illness. Study participants (N = 61) randomly assigned to the SC-R group (n = 29) were offered 24 weeks of no cost Nicotine Replacement Treatment (NRT); participants assigned to the SC + group (n = 32) were offered 24 weeks of no cost NRT plus two initial individual counselling sessions of motivational interviewing and weekly psychosocial group support for 24 weeks. At 6 months the smoking cessation outcome was 7% for the SC-R group and 14% for the SC + group, but there was no statistically significant difference between the groups. Both groups showed a significant decrease in the number of cigarettes smoked per day and significant improvement in physical health functioning. Clients with severe mental illness, high prevalence of co-occurring substance use and experience of homelessness, are both interested and able to quit smoking and reduce cigarette use.

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)0.009
metaresearch head score (Gemma)0.012
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 score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0040.003
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 head0.070
GPT teacher head0.389
Teacher spread0.318 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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