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Record W4289878716 · doi:10.47678/cjhe.v52i2.189145

Facilitators and Barriers Highlighted by On-Campus Service Providers for Students Seeking Mental Health Services

2022· article· en· W4289878716 on OpenAlexaffvenueabout
Hana MacDonald, Konrad Lisnyj, Andrew Papadopoulos

Bibliographic record

VenueCanadian Journal of Higher Education · 2022
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMental healthThematic analysisIncentiveService providerEquity (law)Help-seekingPsychologyMedical educationNursingQualitative researchService (business)MedicinePsychiatryBusinessMarketingSociologyPolitical science

Abstract

fetched live from OpenAlex

The prevalence of mental illness is increasing among post-secondary students. Despite more mental health services being of-fered within post-secondary institutions, uptake among students remains suboptimal. This study aimed to examine facilitators and barriers for students seeking mental health services through service providers’ perspectives. Twenty-four semi-structured interviews were conducted at a southwestern Ontario post-secondary institution and were analyzed using thematic analysis using NVivo. Facilitators revealed include strengthening communication techniques; improving equity, diversity, and inclu-sion; increasing social media promotion; and providing incentives. Barriers identified include fear of judgement, time con-straints, individual perceptions of the need for services, unawareness, and higher-level barriers such as lowered capacity of staff and physical resources. These facilitators and barriers should be used in tandem with the Theory of Planned Behaviour to help improve uptake and effectiveness of campus mental health 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 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)0.020
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.072
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.361
Teacher spread0.347 · 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 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

Citations7
Published2022
Admission routes3
Has abstractyes

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