Tobacco harm reduction strategies: The case for physical activity
Bibliographic record
Abstract
Since the effects of tobacco smoke are so detrimental to health, growing consideration has been given to the development of harm reduction strategies for those smokers who are unable or unwilling to stop using tobacco. The term harm reduction refers to a policy, strategy, or particular intervention that assumes continued use of an undesired behavior and aspires to lower the risk of adverse consequences associated with the continuation of this addictive behavior. Up to this point, tobacco harm reduction interventions have focused on reducing tobacco-related harm through the utilization of innovative tobacco products, reduced tobacco consumption, and pharmaceutical medications. With the possible exception of medicinal nicotine products, these strategies remain unproven and thus far no scientific or medical literature exists to suggest these harm reduction strategies reduce tobacco-related exposure, morbidity, or mortality. Consequently, a need exists for broadening the range of potentially effective harm reduction strategies. This preliminary review suggests that physical activity has the potential to become one such strategy. Of the eight principles that characterize a harm reduction strategy, all are at least partially satisfied by physical activity. Further, emerging evidence indicates that physical activity may delay the occurrence of disease and premature death initiated by tobacco consumption. Significant concerns remain regarding the practicality of physical activity as a harm reduction strategy and the extent to which participation in physical activity may be used to justify continued smoking.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".