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Record W1750882568 · doi:10.1186/1477-7517-3-15

Deconstructing anti-harm-reduction metaphors; mortality risk from falls and other traumatic injuries compared to smokeless tobacco use

2006· article· en· W1750882568 on OpenAlexaff
Carl V. Phillips, Brian Guenzel, Paul Bergen

Bibliographic record

VenueHarm Reduction Journal · 2006
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHarm reductionSmokeless tobaccoHarmHealth psychologyTobacco harm reductionPsychologySocial psychologyMedicineCriminologyEnvironmental healthPublic healthTobacco controlTobacco useNursing

Abstract

fetched live from OpenAlex

Anti-harm-reduction advocates sometimes resort to pseudo-analogies to ridicule harm reduction. Those opposed to the use of smokeless tobacco as an alternative to smoking sometimes suggest that the substitution would be like jumping from a 3 story building rather than 10 story, or like shooting yourself in the foot rather than the head. These metaphors are grossly inappropriate for several reasons, notably including the fact that they are misleading about the actual risk levels. Based on the available literature on mortality from falls, we estimate that smoking presents a mortality risk similar to a fall of about 4 stories, while mortality risk from smokeless tobacco is no worse than that from an almost certainly non-fatal fall from less than 2 stories. Other metaphors are similarly misleading. These metaphors, like other false and misleading anti-harm-reduction statements are inherently unethical attempts to prevent people from learning accurate health information. Moreover, they implicitly provide bad advice about health behavior priorities and are intended to persuade people to stick with a behavior that is more dangerous than an available alternative. Finally, the metaphors exhibit a flippant tone that seems inappropriate for a serious discussion of health science.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.123
GPT teacher head0.443
Teacher spread0.320 · 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.

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

Citations9
Published2006
Admission routes1
Has abstractyes

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