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Record W26732677 · doi:10.1038/mi.2015.135

Smoking on the Margins: An Equity Analysis of Vancouver's Outdoor Smoke-free Policy in Parks and on Beaches

2013· article· en· W26732677 on OpenAlexaboutno aff
Chizimuzo T.C. Okoli, Ann Pederson

Bibliographic record

VenueMucosal Immunology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer Institute
KeywordsEquity (law)SmokeGeographyPolitical scienceMeteorologyLaw

Abstract

fetched live from OpenAlex

Background: Increasingly, jurisdictions worldwide are addressing smoking restrictions in outdoor public spaces to reduce secondhand smoke exposure, discourage youth initiation, enhance cessation, and reduce environmental hazards (i.e., fire and cigarette-related litter). However, there is little research on the policy context and health-equity impact of such policies to help guide wider implementation efforts.\nObjectives: On September 1, 2010, the Board of Parks and Recreation in Vancouver, Canada, introduced a smoke-free bylaw for the city’s parks and beaches. The Smoking on the Margins project is examining the policy context and potential health-equity impact of this bylaw.\nMethods: Applying critical multiplism and equity-focused health impact assessment frameworks, mixed-methods research was used to describe the context and examine health-equity impacts of the bylaw through seven sub-projects.\nPreliminary Results: \nSub-project 1: An observation study (N=6 parks/beaches) found significant reductions in the overall observed smoking rates in selected parks/beaches from prelaw (mean rate=20.5/1000 persons) to 12-months post-law (mean rate=4.7/1000 persons).\nSub-Project 2: A population telephone-survey (N=500 participants) found that 84% of Vancouver residents endorsed the bylaw; smokers were significantly less likely to do so.\nSub-project 3: Two enforcement officer focus-group interviews (n=6 officers/focus-group) found that enforcement practices varied on the basis of park/beach setting, usage patterns, and the likelihood of users to comply. Marginalized populations of smokers were somewhat less likely to be fined for violating the bylaw.\nSub-project 4: Key informant interviews in the cities of Vancouver (n=8), Kelowna (n=5), and Surrey (n=4) found that health, environmental and social concerns are common to all jurisdictions considering implementing an outdoor smoke-free policy but to different degrees.\nSub-project 5: A print-media study (N=90 articles/letters, retrieved Jan2010 to Dec2011) found that in relation to article slant, 38.9% had positive coverage of the bylaw, 30% were neutral, and 22.2% were negative. News articles were more likely to be positive, letters to be negative.\nSub-project 6: A beach-litter study (N=48 beaches/parks) found non-significant changes in cigarette-related litter between 2010 (mean=1018.7 cigarette-butts/filters) and 2011 (mean=919.6 cigarette-butts/filters).\nSub-project 7: A by-law citations study (Jan2011-Dec2011) indicates that citations were issued more frequently at beaches (n=26) than parks (n=12).\nConclusion: Our data suggest that though the outdoor smoke-free policy had strong support in Vancouver, it also had differential effects for park and beach users. Understanding the impact of the policy on diverse groups can minimize potential unintended consequences of outdoor smoke-free policies while providing directions and considerations to help make similar policies more equitable.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.425

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2013
Admission routes1
Has abstractyes

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