An examination of the effectiveness of health warning labels on smokeless tobacco products in four states in India: findings from the TCP India cohort survey
Bibliographic record
Abstract
BACKGROUND: In 2009, after many delays and changes, India introduced a single pictorial health warning label (HWL) on smokeless tobacco (SLT) packing-a symbolic image of a scorpion covering 40% of the front surface. In 2011, the scorpion was replaced with 4 graphic images. This paper tested the effectiveness of SLT HWLs in India and whether the 2011 change from symbolic to graphic images increased their effectiveness. METHODS: Data were from a cohort of 4733 adult SLT users (age15+) of the Tobacco Control Project (TCP) India Survey from 4 states. The surveys included key indicators of health warning effectiveness, including warning salience, and cognitive, emotional, and behavioral responses to the warnings. RESULTS: The HWL change from symbolic to graphic did not result in significant increases on any of the HWL outcome indicators. A substantial minority of SLT users were unaware that SLT packages contained HWLs (27% at both waves). Noticing the warnings was also remarkably low at both waves (W1 = 34.3%, W2 = 28.1%). These effects carried over to the cognitive and behavioural measures, where among those who noticed HWLs, about one-third reported forgoing SLT at least once because of the HWLs, and fewer than 20% reported that HWLs made them think about SLT risks or about quitting SLT. Even fewer reported avoiding HWLs (8.1 to 11.6%). Among those who quit using SLT by post-policy, awareness that SLT packaging contained HWLs was significantly greater at post-policy (86.8%) compared to pre-policy (77.8%, p = 0.02). Quitters were also significantly more aware of the post-policy HWLs compared to those who continued to use SLT (p < 0.001). CONCLUSIONS: Health warnings on SLT packages in India are low in effectiveness, and the change from the symbolic warning (pre-policy) to graphic HWLs (post-policy) did not lead to significant increases of effectiveness on any of the HWL indicators among those who continued to use SLT products, thus suggesting that changing an image alone is not enough to have an impact. There is a critical need to implement SLT HWLs in India that are more salient (large in size and on the front and back of the package) and impactful, which following from studies of HWLs on cigarette packaging, would have strong potential to increase awareness of the harms of SLT and to motivate quitting.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".