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Record W2792211433 · doi:10.1016/j.pmedr.2018.01.011

Impact of smoke-free housing policy lease exemptions on compliance, enforcement and smoking behavior: A qualitative study

2018· article· en· W2792211433 on OpenAlexafffundabout
Pamela Kaufman, Julie Kang, Ryan David Kennedy, Pippa Beck, Roberta Ferrence

Bibliographic record

VenuePreventive Medicine Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsCentre for Addiction and Mental HealthOntario Tobacco Research UnitUniversity of Toronto
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchCanadian Cancer SocietyOntario Ministry of Health and Long-Term Care
KeywordsEnforcementLeaseBusinessCompliance (psychology)Focus groupAffordable housingEnvironmental healthFinanceEconomic growthMedicineMarketingPolitical scienceEconomicsPsychologyLaw

Abstract

fetched live from OpenAlex

This paper investigates the impacts of smoke-free housing policies on compliance, enforcement and smoking behavior. From 2012 to 2014, we studied two affordable housing providers in Canada with comprehensive smoke-free policies: Waterloo Regional Housing that required new leases to be non-smoking and exempted existing leases, and Yukon Housing Corporation that required all leases (existing and new) to be non-smoking. Focus groups and key informant interviews were conducted with 31 housing and public health staff involved in policy development and implementation, and qualitative interviews with 56 tenants. Both types of smoke-free policies helped tenants to reduce and quit smoking. However, exempting existing tenants from the policy created challenges for monitoring compliance and enforcing the policy, and resulted in ongoing tobacco smoke exposure. Moreover, some new tenants were smoking in exempted units, which undermined the policy and maintained smoking behavior. Our findings support the implementation of complete smoke-free housing policies that do not exempt existing leases to avoid many of the problems experienced by staff and tenants. In jurisdictions where exempting existing leases is still required by law, adequate staff resources for monitoring and enforcement, along with consistent and clear communication (particularly regarding balconies, patios and outdoor spaces) will encourage compliance.

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 score0.022
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.125
GPT teacher head0.481
Teacher spread0.357 · 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

Citations25
Published2018
Admission routes3
Has abstractyes

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