A combined community health worker and text messaging-based intervention for smoking cessation in India: Project MUKTI – A mixed methods study
Bibliographic record
Abstract
INTRODUCTION: We sought to evaluate the effectiveness of a community health worker (CHW) led smoking cessation intervention, supplemented by text messages, and tailored to an individual's readiness to quit. METHODS: We conducted a cluster randomized controlled trial (April 2018-August 2019) in adult smokers residing in a semi-urban region of India. Participants in the intervention arm received CHW-led home visits and had the option of choosing to receive regular text messages. The dose and content of CHW counseling and text messages were tailored to the participant's readiness to quit. The control group received brief education only. Primary outcome was biochemically verified smoking cessation at the end of 12 months. Both intention-to-treat and as-treated analyses were performed. RESULTS: A total of 238 (mean age 43±12.3 years, male 96.2%) participants were enrolled; 151 (64%) in the intervention arm and 83 (35.4%) in the control arm. At 12 months, 31 (20.5%) participants in the intervention arm and 9 (10.8%) in the control arm quit smoking (absolute risk difference=9.7%; RR=1.69; 95% CI: 0.04-71.33, p=0.74). In the as-treated analysis, 17 (36.9%) of the 46 participants who received optimal dose of the intervention quit smoking. CONCLUSIONS: CHW-led home-based counseling, supplemented by regular text messages, led to an increase in quit rates for smoking, especially among those exposed to a higher dose of the intervention. However, the difference in cessation rates was not statistically significant. Future studies should consider testing mobile application-based multimedia messaging with larger populations, as a supplement to CHW-based counseling.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".