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Record W2912695337 · doi:10.7870/cjcmh-2018-017

The Caring Campus Project Overview

2018· article· en· W2912695337 on OpenAlexaffvenueabout
Heather Stuart, Shu‐Ping Chen, Terry Krupa, Tasha Narain, Salinda Horgan, Keith S. Dobson, Sherry H. Stewart

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsDalhousie UniversityUniversity of CalgaryUniversity of AlbertaQueen's University
FundersMovember Foundation
KeywordsMental healthEmpowermentSubstance usePsychologyIntervention (counseling)Medical educationPublic relationsPolitical scienceMedicinePsychiatry

Abstract

fetched live from OpenAlex

The Caring Campus project was a three-year intervention research project funded by Movember Canada that fostered new awareness regarding the interconnection between gender, mental health, and substance (specifically alcohol) misuse on three university campuses in Canada, and encouraged new approaches to promote young men’s health. In this project, we demonstrated that male students are willing to assume leadership roles to promote mental health and healthier alcohol use to their peers and enact a social agenda for change. Empowerment strategies encouraged male students to enlist like-minded peers to advance men’s mental health and transform campus drinking cultures, including countering gender-based ideals and norms associated with mental health problems and substance misuse. There is now great potential to influence the way in which other post-secondary institutions approach mental wellness and substance misuse using the Caring Campus model, which uses student empowerment to catalyze change.

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)0.006
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: none
Teacher disagreement score0.168
Threshold uncertainty score0.334

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.002
Scholarly communication0.0060.002
Open science0.0020.011
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0850.020

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 head0.683
GPT teacher head0.675
Teacher spread0.008 · 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

Citations12
Published2018
Admission routes3
Has abstractyes

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