Feasibility study of a smoking cessation intervention in Directly Observed Therapy Short-Course tuberculosis treatment clinics in Rio de Janeiro, Brazil
Bibliographic record
Abstract
A pilot feasibility study was conducted to determine whether Directly Observed Therapy Short-Course (DOTS) workers could be trained to deliver smoking cessation counseling and referral interventions, identify potential barriers to a full-scale randomized controlled trial on the effectiveness of integrated smoking cessation in DOTS, and determine whether tuberculosis (TB) patients who smoke would agree to participate in such a program. DOTS providers in two Rio de Janeiro primary health clinics received 1-day training in cessation counseling. They completed pre- and post-training surveys and participated in post-program focus groups. Patients were surveyed 3 months after program completion, and semiquantitative urine assays for cotinine were used to confirm cessation. Providers' mean self-efficacy scores for cessation counseling improved significantly (advise to quit, assess readiness, assist with quitting, and arrange follow-up) from scores (on a scale of 1-5) of 2-3 pre-training to 3-4 post-training (P < 0.05), with only ability to change motivation not significant. Providers' knowledge about cessation (withdrawal, nicotine replacement therapy, precontemplation) was low before training and did not improve after training (P > 0.1 for all comparisons). Implementation of a smoking cessation intervention by DOTS providers in TB clinics in Brazil is feasible. Randomized controlled trials to test intervention effectiveness in reducing TB-related morbidity must include cross-training for tobacco control and TB providers. Smoking cessation in DOTS programs may be important in reducing the global burden of TB, improving the health of TB patients, and reducing TB transmission in households.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".