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Record W3012214098 · doi:10.18001/trs.6.2.7

Reasons for Supporting or Opposing a Reduced Nicotine Product Standard

2020· article· en· W3012214098 on OpenAlexaff
Jessica K. Pepper, Linda Squiers, Carla Bann, Michaela Coglaiti

Bibliographic record

VenueTobacco Regulatory Science · 2020
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsNicotineAddictionOpposition (politics)Smoking cessationPsychologyTobacco productMedicineSmokeSocial psychologyPsychiatryEnvironmental healthPolitical scienceLaw

Abstract

fetched live from OpenAlex

Objectives: In 2017, the US Food and Drug Administration (FDA) announced a potential new product standard lowering nicotine in cigarettes to minimally or non-addictive levels. Understanding why the public supports or opposes this standard could inform messaging efforts. Methods: We collected online survey data in 2017 from 2508 respondents. We coded and analyzed the open-ended text responses describing reasons for support or opposition among those who strongly agreed (39.9% of sample) and strongly disagreed (11.4%) with the proposed nicotine standard. Results: The most common reasons for opposition were viewing the new standard as a threat to personal freedom and believing that it would lead themselves or others to smoke more. The most common reasons for support were believing the standard would help themselves or others quit smoking and recognizing the harms of smoking and nicotine. Some responses reflected inaccurate understanding of nicotine's effects, and some themes (eg, believing the standard could prevent addiction) were more common among smokers than nonsmokers. Conclusions: Findings could inform public health campaign messages from the FDA and other agencies by building on existing reasons for support (eg, would help with cessation) and counteracting inaccurate beliefs (eg, would make people smoke more).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.350
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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