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Abstract 3: Adapting a Tobacco Cessation Treatment Intervention and Implementation Strategies to Enhance Implementation Effectiveness and Clinical Outcomes in the Context of HIV Care in Viet Nam: A Case Study

2023· article· en· W4379011884 on OpenAlexaff
Donna Shelley, Mari Armstrong‐Hough, Lloyd A. Goldsamt, Gloria Guevera Alvarez, Trang Nguyen, Nam Nguyen

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsYork University
Fundersnot available
KeywordsMedicineQuitlineSmoking cessationContext (archaeology)Psychological interventionNicotine replacement therapyPopulationMotivational interviewingFamily medicineAbstinenceRandomized controlled trialIntervention (counseling)PsychiatryEnvironmental healthInternal medicine

Abstract

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Abstract Purpose: In Vietnam, tobacco use among men living with HIV/AIDS (PLWH) is substantially higher than among the general population. Tobacco use is responsible for significant disparities in cancer-related health outcomes among PLWH who smoke compared with nonsmokers. Despite the burden of disease, there is a lack of evidence for effective treatment for PLWH. We conducted a randomized-controlled trial comparing the effectiveness of three tobacco cessation interventions among PLWH receiving care in HIV outpatient clinics (OPCs). Methods: Patients identified as current cigarette and dual use smokers at the time of their visit (waterpipe and cigarettes) (n=288) were recruited and randomized into one of three interventions: 1) 3As (Ask about tobacco use, Advise to quit, Assist with brief counseling delivered by physicians)+R (proactively Refer to Vietnam’s National Smoker’s Quitline); 2) 3As+Counsel (6-session counseling intervention tailored to PLWH and delivered by trained nurses); and 3) 3As+Counsel+N (nicotine replacement therapy). The primary outcome is six-month carbon monoxide-confirmed smoking abstinence. We are also assessing hypothesized mediators (e.g., self-efficacy, social support). A formative assessment guided adaptation of the intervention to the local context and population. Results: Mean age was 42.4, 96% of patient were male, 8% live alone, 91% are employed, 53.8% were dual users, mean # cigarettes smoked per day was 15.9, and 21.9% reported drug use in the past 3 months. There were no differences across arms in baseline rates of clinical depression or substance use. At 3 months (n=211, those eligible to date), 45% in Arm 1 and 2, and 43.5% in Arm 3, reported smoking abstinence. Among those eligible and reached (98%) for the 6-month survey, 35.9%, 28.9% and 38.3% reported abstinence respectively. Six-month CO- confirmed quit rates were 15.1, 13.3 and 21.3% respectively. 91% of patient received at least one Quitline call and 94% completed 6 sessions of counseling. Discussion: Results are promising and indicate that smoking cessation interventions adapted for PLWH and delivered by health care providers as part of routine care can be effective. The study is the first to demonstrate the feasibility of integrating a Quitline referral system in the context of HIV care. Therefore, the similar outcome across arms has important policy implications; the Quitline is a sustainable resource requiring fewer OPC resources than more intensive counseling. Citation Format: Donna Shelley, Mari Armstrong-Hough, Lloyd Goldsamt, Gloria Guevera Alvarez, Trang Nguyen, Nam Nguyen. Adapting a Tobacco Cessation Treatment Intervention and Implementation Strategies to Enhance Implementation Effectiveness and Clinical Outcomes in the Context of HIV Care in Viet Nam: A Case Study [abstract]. In: Proceedings of the 11th Annual Symposium on Global Cancer Research; Closing the Research-to-Implementation Gap; 2023 Apr 4-6. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(6_Suppl):Abstract nr 3.

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)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.080
GPT teacher head0.522
Teacher spread0.443 · 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 designCase report
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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