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Record W2061740285 · doi:10.1007/s12160-014-9605-z

Effectiveness of the Tobacco Tactics Program in the Department of Veterans Affairs

2014· article· en· W2061740285 on OpenAlexaff
Sonia A. Duffy, David L. Ronis, Carrie Karvonen‐Gutierrez, Lee A Ewing, Gregory W. Dalack, Patricia M. Smith, Timothy P. Carmody, Thomas Hicks, Christopher Hermann, Pamela Reeves, Petra Flanagan

Bibliographic record

VenueAnnals of Behavioral Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsNOSM University
FundersNational Institutes of HealthU.S. Department of Veterans Affairs
KeywordsVeterans AffairsCotinineMedicineIntervention (counseling)Report cardHealth psychologySmoking cessationFamily medicinePhysical therapyNursingNicotinePublic healthPsychiatryPsychology

Abstract

fetched live from OpenAlex

PURPOSE: The purpose was to determine the effectiveness of the Tobacco Tactics program in three Veterans Affairs hospitals. METHODS: In this effectiveness trial, inpatient nurses were educated to provide the Tobacco Tactics intervention in Ann Arbor and Detroit, while Indianapolis was the control site (N = 1,070). Smokers were surveyed and given cotinine tests. The components of the intervention included nurse counseling, brochure, DVD, manual, pharmaceuticals, 1-800-QUIT-NOW card, and post-discharge telephone calls. RESULTS: There were significant improvements in 6-month quit rates in the pre- to post-intervention time periods in Ann Arbor (p = 0.004) and Detroit (p < 0.001) compared to Indianapolis. Pre- versus post-intervention quit rates were 4 % compared to 13 % in Detroit, were similar (6 %) pre- and post-intervention in Ann Arbor, and dropped from 26 % to 12 % in Indianapolis. CONCLUSION: The Tobacco Tactics program, which meets the Joint Commission standards that apply to all inpatient smokers, has the potential to significantly decrease smoking among Veterans.

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 score0.062
Threshold uncertainty score0.252

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.085
GPT teacher head0.407
Teacher spread0.322 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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