Perceived effectiveness of text and pictorial health warnings for smokeless tobacco packages in Navi Mumbai, India, and Dhaka, Bangladesh: findings from an experimental study
Bibliographic record
Abstract
OBJECTIVE: To examine the perceived effectiveness of text and pictorial smokeless tobacco health warnings in India and Bangladesh, including different types of message content. METHODS: An experimental study was conducted in Navi Mumbai, India (n=1002), and Dhaka, Bangladesh (n=1081). Face-to-face interviews were conducted on tablets with adult (≥19 years) smokeless tobacco users and youth (16-18 years) users and non-users. Respondents viewed warnings depicting five health effects, within one of the four randomly assigned warning label conditions (or message themes): (1) text-only, (2) symbolic pictorial, (3) graphic pictorial or (4) personal testimonial pictorial messages. RESULTS: Text-only warnings were perceived as less effective than all of the pictorial styles (p<0.001 for all). Graphic warnings were given higher effectiveness ratings than symbolic or testimonial warnings (p<0.001). No differences were observed in levels of agreement with negative attitudes and beliefs across message themes, after respondents had viewed warnings. CONCLUSIONS: Pictorial warnings are more effective than text-only messages. Pictorial warnings depicting graphic health effects may have the greatest impact, consistent with research from high-income countries on cigarette warnings.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".