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Record W4401163313 · doi:10.1080/07448481.2024.2378291

Examining the experiences of student peer support workers delivering care within post-secondary institutions

2024· article· en· W4401163313 on OpenAlexaff
Gina Dimitropoulos, Emma Cullen, Julia Hews‐Girard, Scott B. Patten, Pauline MacPherson, Jai Shah, Kevin Friese, Kevin Wiens, Bonny Lipton-Bos, Helen Vallianatos, Andrew C. H. Szeto, Manuela Ferrari, Srividya N. Iyer

Bibliographic record

VenueJournal of American College Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCanadian Mental Health AssociationCalgary Laboratory ServicesUniversity of CalgaryMcGill UniversityDouglas Mental Health University InstituteUniversity of Alberta
FundersOPEN MINDS
KeywordsPeer supportPeer groupPsychologySocial supportMedicineNursingMedical educationSocial psychology

Abstract

fetched live from OpenAlex

Objective: Despite experiencing exacerbation of mental health issues, post-secondary students may not seek help due to perceived stigma, overreliance on the self, or preference for nonprofessional supports – including peer support. This study aimed to understand peer support workers’ (PSWs) perspectives regarding providing support for mental health concerns in post-secondary institutions. Methods: 41 PSWs were recruited from two post-secondary institutions. 17 semi-structured interviews and three focus groups were conducted. Themes were identified using a qualitative descriptive approach. Results: Three themes emerged: (1) diverse presentations and approaches to operationalizing peer support for mental health issues on campus exist; (2) peer support has core ingredients; (3) reasons why students access peer support extend beyond mental health crisis. Conclusions: An inclusive peer support approach to mental health is needed for post-secondary students. Considerations for implementation hinge on providing standardized, foundational training to prepare PSWs for the complex mental health issues that present across services.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.183
GPT teacher head0.457
Teacher spread0.273 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2024
Admission routes1
Has abstractyes

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