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Record W1989888317 · doi:10.1080/16506073.2011.632437

Nature and Role of Change in Anxiety Sensitivity During NRT-Aided Cognitive-Behavioral Smoking Cessation Treatment

2012· article· en· W1989888317 on OpenAlexaff
Yaara Assayag, Amit Bernstein, Michael J. Zvolensky, Dan Steeves

Bibliographic record

VenueCognitive Behaviour Therapy · 2012
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsDalhousie UniversityCapital District Health Authority
FundersNational Institute on Drug AbuseNational Institute of Mental Health
KeywordsSmoking cessationAnxiety sensitivityAnxietyClinical psychologyCognitionPsychologyIntervention (counseling)MedicinePsychiatry

Abstract

fetched live from OpenAlex

This study evaluated the associations between change in anxiety sensitivity (AS; fear of the negative consequences of anxiety and related sensations) and lapse and relapse during a 4-week group NRT-aided cognitive-behavioral Tobacco Intervention Program. Participants were 67 (44 women; M (age) = 46.2 years, SD = 10.4) adult daily smokers. Results indicated that participants who maintained high levels of AS from pretreatment to 1 month posttreatment, compared to those who demonstrated a significant reduction in AS levels during this time period, showed a significantly increased risk for lapse and relapse. Further inspection indicated that higher continuous levels of AS physical and psychological concerns, specifically among those participants who maintained elevated levels of AS from pre- to posttreatment, predicted significantly greater risk for relapse. Findings are discussed with respect to better understanding change in AS, grounded in an emergent taxonic-dimensional factor mixture model of the construct, with respect to lapse and relapse during smoking cessation.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.359
Teacher spread0.285 · 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

Citations58
Published2012
Admission routes1
Has abstractyes

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