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Record W4416203400 · doi:10.1177/00914509251393876

Alcohol in Context: Alcohol Experiences Among First-Year Residence-Dwelling Students at Two Canadian Universities

2025· article· en· W4416203400 on OpenAlexafffundabout
Alex Liaukovich, Amrit Kaur Matharoo, Victoria Burns, Niki Kiepek

Bibliographic record

VenueContemporary Drug Problems · 2025
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of CalgaryDalhousie University
FundersCanadian Institutes of Health Research
KeywordsHarmQualitative researchHarm reductionAlcohol consumptionPromotion (chess)Health promotionHeavy drinkingEconomic JusticeQualitative analysis

Abstract

fetched live from OpenAlex

While extensive research has examined alcohol-related harms in university residences, few studies have explored how specific drinking contexts-such as physical location, policies, and broader socio-cultural factors-shape student alcohol use. This qualitative study utilized semi-structured interviews with 25 first-year residential students and 16 staff across two Canadian universities (University of Calgary, Alberta, and Dalhousie University, Nova Scotia) to investigate how intersecting social, physical, economic, and policy environments contribute to university residences as risk and/or enabling environments. Drawing on Rhodes's risk environment framework, the analysis identified three themes: (1) Navigating tensions between policies and the physical and economic environments; (2) Shaping alcohol experiences through social influences; and (3) Co-creating cultures of care. This research highlights how student residences can reduce alcohol-related harms while fostering cultures of care, health, and well-being. These findings provide deeper insights into peer influence, the promotion of enabling environments, and integrating restorative justice as part of harm reduction strategies.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0120.005
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
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 head0.032
GPT teacher head0.296
Teacher spread0.264 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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Same venueContemporary Drug ProblemsSame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207