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Record W2510817846 · doi:10.1186/s12889-016-3466-2

Smoking on the margins: a comprehensive analysis of a municipal outdoor smoke-free policy

2016· article· en· W2510817846 on OpenAlexafffundabout
Ann Pederson, Chizimuzo T.C. Okoli, Natalie Hemsing, Renée O’Leary, Amanda T. Wiggins, Wendy Rice, Joan L. Bottorff, Lorraine Greaves

Bibliographic record

VenueBMC Public Health · 2016
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of VictoriaBritish Columbia Centre of Excellence for Women's HealthB.C. Women's Hospital & Health Centre
FundersInstitute of Population and Public HealthCanadian Institutes of Health Research
KeywordsEnforcementBiostatisticsPublic healthEquity (law)Environmental healthMedicineHealth policyCompliance (psychology)Political scienceNursingPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: This study examined the formulation, adoption, and implementation of a ban on smoking in the parks and beaches in Vancouver, Canada. METHODS: Informed by Critical Multiplism, we explored the policy adoption process, support for and compliance with a local bylaw prohibiting smoking in parks and on beaches, experiences with enforcement, and potential health equity issues through a series of qualitative and quantitative studies. RESULTS: Findings suggest that there was unanimous support for the introduction of the bylaw among policy makers, as well as a high degree of positive public support. We observed that smoking initially declined following the ban's implementation, but that smoking practices vary in parks by location. We also found evidence of different levels of enforcement and compliance between settings, and between different populations of park and beach users. CONCLUSIONS: Overall success with the implementation of the bylaw is tempered by potential increases in health inequities because of variable enforcement of the ban; greatest levels of smoking appear to continue to occur in the least advantaged areas of the city. Jurisdictions developing such policies need to consider how to allocate sufficient resources to enhance voluntary compliance and ensure that such bylaws do not contribute to health inequities.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.149
GPT teacher head0.383
Teacher spread0.234 · 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

Citations44
Published2016
Admission routes3
Has abstractyes

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