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Record W2903238717 · doi:10.18785/ojhe.1402.05

Nicotine Vaccines for Smoking Prevention and Treatment from Utilitarian and Deontological Ethical Perspectives

2018· article· en· W2903238717 on OpenAlexaff
Enam Alsrayheen, Khaldoun Aldiabat

Bibliographic record

VenueJournal of Health Ethics · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsHarmUtilitarianismDeontological ethicsNicotineAddictionMedicinePerspective (graphical)Cigarette smokePublic healthPsychiatryPsychologyEnvironmental healthSocial psychologyPolitical scienceNursingLaw

Abstract

fetched live from OpenAlex

Nicotine vaccines are a new prevention and treatment method for smoking addiction. They are promoted as a method to cease smoking among those who smoke and possibly prevent this behaviour from taking place among those who do not smoke. However, offering these vaccines to adults, adolescents, and children will undoubtedly raise an ethical debate among policy-makers, health professionals, and the public. This paper discusses the possibility of using nicotine vaccines treat and prevent smoking among adults/children/adolescents through the lenses of two ethical theories: utilitarianism and deontology (Kantianism). From an utilitarian perspective, nicotine vaccines are good for society because they provide the greatest benefit for the greatest number of individuals. Authors perceive them as a healthy ethical choice to prevent and treat smoking. And, from the deontological perspective, nicotine vaccines are justified because individuals can prevent the harm of nicotine addiction by choosing vaccines or any other smoking prevention and treatment methods.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.149
GPT teacher head0.460
Teacher spread0.311 · 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 designObservational
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

Citations3
Published2018
Admission routes1
Has abstractyes

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