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Record W2026961879 · doi:10.1111/phn.12068

Sharing the Air, the Legal and Ethical Considerations of Extending Tobacco Legislation to Include Multiunit Dwellings in Alberta

2013· article· en· W2026961879 on OpenAlexaffabout
Victoria D. Stooke

Bibliographic record

VenuePublic Health Nursing · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsFoothills Medical Centre
Fundersnot available
KeywordsLegislationPublic healthPolitical scienceBureaucracyPublic administrationTobacco smokeAccountabilityEnvironmental healthPoliticsLawMedicineNursing

Abstract

fetched live from OpenAlex

This study explores the legal and ethical considerations of extending tobacco legislation to include multiunit dwellings (MUDs) in Alberta and the implications for public health nursing practice. The tobacco legislation in Canada currently protects individuals in public places and not private dwellings. In Alberta, there are over 1 million individuals living in MUDs who are exposed to environmental tobacco smoke. Children are particularly vulnerable to the negative health effects. As well, many apartment fires in Alberta are related to smoking which makes expanding tobacco legislation to include MUDs an important public health issue. There are many potential barriers to the adoption of this tobacco legislation including legal, ethical, and civil rights concerns, and the bureaucracy of the political process. This study articulates the position that it is both legal and ethical to expand provincial tobacco legislation to include MUDs after the consideration of individual civil rights and using the Canadian Nurses Association Code of Ethics for Registered Nurses (2008) as a guide for practice. Public health nurses must advocate for a change in the current legislation by becoming politically active and building community capacity to demonstrate accountability and promote social justice.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.625
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.070
GPT teacher head0.379
Teacher spread0.309 · 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

Citations0
Published2013
Admission routes2
Has abstractyes

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