Moving multiunit housing providers toward adoption of smoke-free policies.
Bibliographic record
Abstract
BACKGROUND: Tenants in multiunit housing are at elevated risk for exposure to secondhand smoke at home because of smoke migration from other units. COMMUNITY CONTEXT: In 2004, tobacco control advocates in the Portland, Oregon, metropolitan area began to address this issue by launching a campaign to work with landlord and tenant advocates, private- and public-sector property managers, and other housing stakeholders to encourage smoke-free policies in multiunit housing. METHODS: We outline the 6-year campaign that moved local housing providers toward adopting no-smoking policies. We used the stages of change model, which matches potential messages or interventions to a smoker's readiness to quit smoking. OUTCOME: The campaign resulted in Oregon's largest private property management company and its largest public housing authority adopting no-smoking policies for their properties and a 29% increase in the availability of smoke-free rental units in the Portland-Vancouver metro area from 2006 through 2009. INTERPRETATION: We learned the importance of building partnerships with public and private stakeholders, collecting local data to shape educational messages, and emphasizing to landlords the business case, not the public health rationale, for smoke-free housing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".