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Record W2999503780 · doi:10.3390/ijerph17020396

Gender Informed or Gender Ignored? Opportunities for Gender Transformative Approaches in Brief Alcohol Interventions on College Campuses

2020· review· en· W2999503780 on OpenAlexafffund
Lindsay Wolfson, Julie Stinson, Nancy Poole

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2020
Typereview
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsBritish Columbia Centre of Excellence for Women's Health
FundersInstitute of Gender and HealthCanadian Institutes of Health Research
KeywordsPsychological interventionTransformative learningHarmInclusion (mineral)PsychologyMedicineSocial psychologyPsychiatryDevelopmental psychology

Abstract

fetched live from OpenAlex

Brief alcohol interventions are an effective strategy for reducing harmful and risky alcohol use and misuse. Many effective brief alcohol interventions include information and advice about an individual's alcohol use, changing their use, and assistance in developing strategies and goals to help reduce their use. Emerging research suggests that brief interventions can also be expanded to address multiple health outcomes; recognizing that the flexible nature of these approaches can be helpful in tailoring information to specific population groups. This scoping review synthesizes evidence on the inclusion of sex and gender in brief alcohol interventions on college campuses, highlighting available evidence on gender responsiveness in these interventions. Furthermore, this scoping review offers strategies on how brief alcohol interventions can be gender transformative, thereby enhancing the effectiveness of brief alcohol interventions as harm reduction and prevention strategies, and in promoting gender equity.

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)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.736
GPT teacher head0.509
Teacher spread0.227 · 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 designNot applicable
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

Citations7
Published2020
Admission routes2
Has abstractyes

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