A Harm-Based Classification of Preps: In Support of an Effective Policy Debate on Potential Reduced-Exposure Products (Preps), Tobacco and Tobacco Replacement Products
Bibliographic record
Abstract
This paper develops a draft harm-based classification of so-called potential reduced-exposure (PREPs) as a tool in the discussion of the utility of these products in reduction strategies, and in the broader discussions over nicotine and tobacco regulatory and policy development. To this end, the paper develops definitions of the terms PREP and and provides an inquiry into the concept harm reduction. Following from this, the concept of a profile for PREPs is developed. The profile used in the paper contains the following ten elements: morbidity, mortality, problematic addiction, to the health or longevity of bystanders, to the health of children or foetuses, differential according to gender, differential according to racial or aboriginal origin, differential according to economic status or age, cost to the health care system, and increase in societal tobacco usage rate. In other words, it is a system that cascades from the to users, to bystanders, to groups and, finally, to society. The classification project was carried out in three steps. First, we listed the PREPs that should be classified within a harm-based classification. Because of the large number of products in development or on the market, it was necessary to develop a basic classification of PREPs. In a second step, profiles were developed for each of the approximately 60 classes in the basic classifications. Finally, based on the similarities observed in the profiles of the various classes, a new, harm-based classification was developed. This resulted in 10 harm-based classes of products. The paper concludes with a list of 10 discussion points that the tobacco control community may want to consider, and with ideas for future research.See http://papers.ssrn.com/abstract=1559407 for supporting diagram A Basic Classification of PREPs: Understood in the Widest Sense and Grouped with Conventional Tobacco Products
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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 | 0.041 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.011 | 0.022 |
| 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".