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Record W2783734045 · doi:10.1186/s12889-018-5061-1

Adoption and diffusion of zoning bylaws banning fast food drive-through services across Canadian municipalities

2018· article· en· W2783734045 on OpenAlexafffundabout
Candace I. J. Nykiforuk, Elizabeth J. Campbell, Soultana Macridis, Daniel McKennitt, Kayla Atkey, Kim D. Raine

Bibliographic record

VenueBMC Public Health · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsAlberta Health ServicesUniversity of Alberta
FundersPartenariat Canadien Contre Le Cancer
KeywordsEarly adopterZoningContext (archaeology)BusinessPromotion (chess)Public administrationMarketingPolitical scienceGeographyLaw

Abstract

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BACKGROUND: Healthy public policy is an important tool for creating environments that support human health and wellbeing. At the local level, municipal policies, such as zoning bylaws, provide an opportunity for governments to regulate building location and the type of services offered. Across North America, there has been a recent proliferation of municipal bylaws banning fast food drive-through services. Research on the utilization of this policy strategy, including bylaw adopters and adopter characteristics, is limited within the Canadian context. The aim of this study was to identify and characterize Canadian municipalities based on level of policy innovation and nature of their adopted bylaw banning fast food drive-through services. METHODS: A multiple case history methodology was utilized to identify and analyse eligible municipal bylaws, and included development of a chronological timeline and map of adopter municipalities within Canada. Grey literature and policy databases were searched for potential adopters of municipal fast food drive-through service bylaws. Adopters were confirmed through evidence of current municipal bylaws. Geographic diffusion and diffusion of innovations theories provided a contextual framework for analysis of bylaw documents. Analysis included assignment of adopter-types, extent and purpose of bans, and policy learning activities of each adopter municipality. RESULTS: From 2002 to 2016, 27 municipalities were identified as adopters: six innovators and twenty-one early adopters. Mapping revealed parallel geographic diffusion patterns in western and eastern Canada. Twenty-two municipalities adopted a partial ban and five adopted a full ban. Rationales for the drive-through bans included health promotion, environmental concerns from idling, community character and aesthetics, traffic concerns, and walkability. Policy learning, including research and consultation with other municipalities, was performed by nine early adopters. CONCLUSION: This study detailed the adoption of fast food drive-through bylaws across Canada. Understanding the adopter-type characteristics of municipalities and the nature of their bylaws can assist other jurisdictions in similar policy efforts. While the implications for research and practice are evolving and dynamic, fast food drive-through service bans may play a role in promoting healthier food environments. Further research is required to determine the viability of this strategy for health promotion and chronic disease prevention.

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)0.004
metaresearch head score (Gemma)0.016
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 score0.085
Threshold uncertainty score0.619

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.011
Science and technology studies0.0060.002
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.067
GPT teacher head0.332
Teacher spread0.265 · 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

Citations26
Published2018
Admission routes3
Has abstractyes

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