Withholding differential risk information on legal consumer nicotine/tobacco products: The public health ethics of health information quarantines
Bibliographic record
Abstract
The United States provides an example of a country with (a) legal tobacco/nicotine products (e.g., snus, other smokeless tobacco, cigarettes) differing greatly in risks to health and (b) respected health information websites that continue to omit or provide incorrect differential risk information. Concern for the principles of individual rights, health literacy, and personal autonomy (making decisions for oneself), which are key principles of public health ethics, has been countered by utilitarian arguments for the use of misleading or limited information to protect public health overall. We argue that omitting key health relevant information for current or prospective consumers represents a kind of quarantine of health-relevant information. As with disease quarantines, the coercive effects of quarantining information on differential risks need to be justified, not merely by fears of net negative public health effects, but by convincing evidence that such measures are actually warranted, that public health overall is in imminent danger and that the danger is sufficient to override principles of individual autonomy. Omitting such health-relevant information for consumers of such products effectively blindfolds them and impairs their making informed personal choices. Moral psychological issues that treat all tobacco/nicotine products similarly may also be influencing the reluctance to inform on differential risks. In countries where tobacco/nicotine products are legally sold and also differ greatly in disease risks compared to cigarettes (e.g., smokeless tobacco and vape), science-based, comprehensible, and actionable health information (consistent with health literacy principles) on differential risks should be available and only reconsidered if it is established that this information is causing losses to population health overall.
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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 | 0.079 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.058 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.033 | 0.025 |
| Insufficient payload (model declined to judge) | 0.002 | 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".