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Record W190274809 · doi:10.15173/m.v1i21.791

University Campus Peer Support Centres: Benefits for Student Emotional and Mental Well-Being

2012· article· en· W190274809 on OpenAlexaffvenue
Ikdip Kaur. Brar, Jae Eun Ryu, Kamran Shaikh, Ashlie Altman, Jeremy Chung Fai Ng

Bibliographic record

VenueThe Meducator · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMental healthPeer reviewPeer supportPsychologyWell-beingApplied psychologyMedical educationEmotional well-beingMedicineClinical psychologyPsychiatryPolitical sciencePsychotherapist

Abstract

fetched live from OpenAlex

Within undergraduate student populations, there has been a rise in the incidence of mental health issues such as depression and anxiety. These problems have been shown to negatively impact emotional wellbeing and academic success.1 Many elements of the undergraduate experience, including stressful transitions from high school to first year, contribute to mental health problems amongst this student body. Peer support is a relatively recent resource for universities to address growing mental health concerns on campus. Peer support, which involves trained students who voluntarily provide emotional support to peers, offers a unique function to student mental health. It can be useful throughout a student’s undergraduate career and is also beneficial to those who provide the support. While it may not replace professional mental health services, it may be a significant addition to the existing student wellness support systems on university campuses today.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.030
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.003

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 head0.022
GPT teacher head0.358
Teacher spread0.336 · 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

Citations8
Published2012
Admission routes2
Has abstractyes

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