MétaCan
Menu
← Back to cohort
Record W2972548569 · doi:10.3390/ijerph16183332

Prevalence of Involuntary Environmental Cannabis and Tobacco Smoke Exposure in Multi-Unit Housing

2019· article· en· W2972548569 on OpenAlexafffundabout
Alanna Chu, Pamela Kaufman, Michael Chaiton

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsOntario Tobacco Research UnitUniversity of Toronto
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term Care
KeywordsCannabisTobacco smokeEnvironmental healthUnit (ring theory)SmokeMarijuana smokingMedicinePsychiatryPsychologySubstance abuseEngineeringWaste management

Abstract

fetched live from OpenAlex

No research has examined the prevalence of involuntary cannabis exposure in the home within the context of multi-unit housing (MUH). The 2017 cycle of the Centre for Addiction and Mental Health Monitor population RDD survey included measures of environmental cannabis smoke (ECS) and environmental tobacco smoke (ETS) for Ontario, Canada. These ECS measures were defined for those who did not live in a detached dwelling self-reporting noticing any tobacco or cannabis smoke enter the home from a neighboring unit or from outside the building at least once in the past 6 months. Overall, 6.6% (95% CI: 4.5-9.5%) and 7.5% (9% CI: 5.4-10.4%) of the population reported being exposed to ETS and ECS in MUH respectively. Individuals exposed to ECS were single, had used cannabis in the past 12 months, and had lower household incomes. The prevalence of involuntary exposure to cannabis smoke is similar to exposure to tobacco smoke. Exposure correlates were primarily associated with characteristics of those who lived in MUH who tend to be members of more vulnerable populations.

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)0.002
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 score0.086
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.138
GPT teacher head0.456
Teacher spread0.318 · 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

Citations10
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public Health→Same topicHomelessness and Social Issues→French-language works237,207→