MétaCan
Menu
Back to cohort
Record W2967875392

The Public Health Implications of Administrative Policy Responses to E-Cigarette Use: A Content Analysis of Ontario’s University Campus Policies

2019· article· en· W2967875392 on OpenAlexaboutno aff
D Stewart Irvine, Aaron O. Bailey

Bibliographic record

VenueUndergraduate Research Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Thematic analysisHarmPublic policyPolitical sciencePublic healthPromotion (chess)Content analysisPublic administrationPolicy analysisPublic relationsMedicineSociologyQualitative researchNursingSocial science
DOInot available

Abstract

fetched live from OpenAlex

Purpose: To investigate Ontario university’s institutional policy responses to the influx in e-cigarette use among adolescents and their associated regulation within campuses. Methods: A review of relevant policy documents among Ontario’s 22 public universities was conducted. A manual search yielded relevant documents which were subjected to individual analysis by the authors. Interpretation and thematic grouping of their respective policies on e-cigarette use was conducted. Results:17 of 22 universities (n=17) have published policy documents relating to e-cigarette use on campus. The remaining 5 universities did not have a publicly available e-cigarette policy or do not mention e-cigarettes within their policy on smoking. Discussion: 5 major thematic groups were identified, falling along a spectrum from blanket bans of e-cigarette use to the failure to reference e-cigarettes in relevant policy. Conclusion: Administrative policies regarding e-cigarette use among Ontario universities are heterogenous and vary dramatically in scope. Future research in the arena of e-cigarette regulation and harm prevention would be beneficial to health promotion on university campuses in Ontario. See complete issue to read the full text.

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)0.002
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 score0.149
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.304
GPT teacher head0.455
Teacher spread0.151 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueUndergraduate Research JournalSame topicSmoking Behavior and CessationFrench-language works237,207