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Record W2165348164 · doi:10.1093/her/cyp026

A review of undergraduate university tobacco control policy process in Canada

2009· review· en· W2165348164 on OpenAlexafffundabout
Lynne Baillie, Doris Callaghan, Michelle L. Smith, Joan L. Bottorff, Joan Bassett‐Smith, Claire Budgen, M. Federsen

Bibliographic record

VenueHealth Education Research · 2009
Typereview
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaBC Cancer Agency
FundersBC Cancer Foundation
KeywordsTobacco controlEnforcementControl (management)Medical educationPublic relationsTobacco industryPolitical scienceMedicinePsychologyNursingManagementPublic healthLaw

Abstract

fetched live from OpenAlex

The college years occur during the stage of life when many people develop permanent smoking habits, and approximately one-third go on to become addicted smokers. The 18-24 year demographic that makes up the majority of undergraduate attendees represents the earliest years that the tobacco industry now can legally attempt to lure new customers into smoking. This research investigated the ways in which university tobacco control policies are developed, introduced to students, faculty and staff and how they are implemented and enforced. Findings show that tobacco control initiatives at Canadian undergraduate universities face a wide range of challenges including a lack of dedicated and consistent tobacco control personnel, ownership issues, funding, enforcement and monitoring dilemmas. Participants also reported that the layout and geographic location of the campus can result in difficulties in implementation. Consequently, it appears that there may be a growing, although inadvertent, tolerance for smoking on Canadian campuses.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score0.367

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0100.025
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.174
GPT teacher head0.539
Teacher spread0.365 · 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 designQualitative
Domainnot available
GenreReview

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

Citations16
Published2009
Admission routes3
Has abstractyes

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